How to use start_time method in locust

Best Python code snippet using locust

index.js

Source:index.js

1const moment = require('moment');2const swapLetters = (t) => {3 return {4 t,5 t1: t[1]+t[0]+t[2],6 t2: t[0]+t[2]+t[1],7 }8}9const countNumberOfCandidates = (T, cand) => {10 let count = 0;11 for (let t of T) {12 if (Object.values(swapLetters(t)).includes(cand))13 count++;14 }15 return count;16}17const Solution = (T) => {18 let output = 019 for (let t of T) {20 for (let cand of Object.values(swapLetters(t))) {21 let sum = countNumberOfCandidates(T, cand);22 output = Math.max(output, sum);23 }24 }25 return output;26}27/​/​ console.log(Solution(["aab", "cab", "baa", "baa"]))28const obj = [29 {30 "node": {31 "id": "804a334b-94da-45a6-8709-5d1b328b735f",32 "relationship_type": "related-to",33 "start_time": "2022-06-13T11:04:11.000Z",34 "stop_time": "2022-06-13T11:04:11.000Z"35 }36 },37 {38 "node": {39 "id": "00a4b6a1-4603-4666-ba23-e4c8caef7917",40 "relationship_type": "related-to",41 "start_time": "2022-06-13T11:03:39.000Z",42 "stop_time": "2022-06-13T11:03:39.000Z"43 }44 },45 {46 "node": {47 "id": "7e3a6192-5b9e-4205-80ac-f37ecbf5f0f5",48 "relationship_type": "related-to",49 "start_time": "2022-06-13T11:03:37.000Z",50 "stop_time": "2022-06-13T11:03:37.000Z"51 }52 },53 {54 "node": {55 "id": "d71a04c7-1be6-45b1-b2c7-10a78de1a241",56 "relationship_type": "related-to",57 "start_time": "2022-06-13T11:02:16.000Z",58 "stop_time": "2022-06-13T11:02:16.000Z"59 }60 },61 {62 "node": {63 "id": "036e4b7f-16e9-431d-9f79-26fe6ffae6a5",64 "relationship_type": "related-to",65 "start_time": "2022-06-13T11:02:15.000Z",66 "stop_time": "2022-06-13T11:02:15.000Z"67 }68 },69 {70 "node": {71 "id": "fd79a148-a6f0-43b4-93c0-c68b724087ee",72 "relationship_type": "related-to",73 "start_time": "2022-06-13T11:02:14.000Z",74 "stop_time": "2022-06-13T11:02:14.000Z"75 }76 },77 {78 "node": {79 "id": "25c20ab1-5356-4b9e-88ec-576a2f802f9a",80 "relationship_type": "related-to",81 "start_time": "2022-06-13T11:02:13.000Z",82 "stop_time": "2022-06-13T11:02:13.000Z"83 }84 },85 {86 "node": {87 "id": "757edd8a-be31-40ca-be36-3b88e0bdc91c",88 "relationship_type": "related-to",89 "start_time": "2022-06-13T11:02:08.000Z",90 "stop_time": "2022-06-13T11:02:08.000Z"91 }92 },93 {94 "node": {95 "id": "7708ffb2-9891-4202-8d3a-8ac0f32a72cd",96 "relationship_type": "related-to",97 "start_time": "2022-06-13T11:02:07.000Z",98 "stop_time": "2022-06-13T11:02:07.000Z"99 }100 },101 {102 "node": {103 "id": "d73c87d7-4fb2-434d-aa1f-ee9e8c110544",104 "relationship_type": "related-to",105 "start_time": "2022-06-13T11:02:05.000Z",106 "stop_time": "2022-06-13T11:02:05.000Z"107 }108 },109 {110 "node": {111 "id": "535ae076-ea69-402d-b53f-91bbbefbbf11",112 "relationship_type": "related-to",113 "start_time": "2022-06-13T11:01:57.000Z",114 "stop_time": "2022-06-13T11:01:57.000Z"115 }116 },117 {118 "node": {119 "id": "c5aecbe3-3b3d-4224-90a0-0caa99341a8c",120 "relationship_type": "related-to",121 "start_time": "2022-06-13T11:01:56.000Z",122 "stop_time": "2022-06-13T11:01:56.000Z"123 }124 },125 {126 "node": {127 "id": "9d4a2f00-b07f-49a3-844c-32c6d305cbb3",128 "relationship_type": "related-to",129 "start_time": "2022-06-13T11:01:55.000Z",130 "stop_time": "2022-06-13T11:01:55.000Z"131 }132 },133 {134 "node": {135 "id": "e94c2b0d-ae60-41b9-942c-9db9f6233d3b",136 "relationship_type": "related-to",137 "start_time": "2022-06-13T11:01:54.000Z",138 "stop_time": "2022-06-13T11:01:54.000Z"139 }140 },141 {142 "node": {143 "id": "b112cb77-07e7-4dc2-8540-83d68654a60b",144 "relationship_type": "related-to",145 "start_time": "2022-06-13T11:01:53.000Z",146 "stop_time": "2022-06-13T11:01:53.000Z"147 }148 },149 {150 "node": {151 "id": "2b255fa2-dd90-47f0-a550-ca38d44d3437",152 "relationship_type": "related-to",153 "start_time": "2022-06-13T11:01:52.000Z",154 "stop_time": "2022-06-13T11:01:52.000Z"155 }156 },157 {158 "node": {159 "id": "72b8279f-fa4f-4d35-adbf-5decf20a8427",160 "relationship_type": "related-to",161 "start_time": "2022-06-13T11:01:50.000Z",162 "stop_time": "2022-06-13T11:01:50.000Z"163 }164 },165 {166 "node": {167 "id": "16d892e0-c167-45da-bed1-8885340cbc37",168 "relationship_type": "related-to",169 "start_time": "2022-06-13T11:01:47.000Z",170 "stop_time": "2022-06-13T11:01:47.000Z"171 }172 },173 {174 "node": {175 "id": "31c9ec9a-d953-4a8c-990d-d5cb395b2761",176 "relationship_type": "related-to",177 "start_time": "2022-06-13T11:01:46.000Z",178 "stop_time": "2022-06-13T11:01:46.000Z"179 }180 },181 {182 "node": {183 "id": "dd4d3a72-6e76-4fac-a90e-2b260a9921b4",184 "relationship_type": "indicates",185 "start_time": "2022-06-13T10:47:56.000Z",186 "stop_time": "2022-06-13T10:47:56.000Z"187 }188 },189 {190 "node": {191 "id": "ff6a923e-db28-4393-ac09-2a759fe82b3d",192 "relationship_type": "related-to",193 "start_time": "2022-06-13T10:47:56.000Z",194 "stop_time": "2022-06-13T10:47:56.000Z"195 }196 },197 {198 "node": {199 "id": "f456f0b1-c305-4072-989d-a7c5a468ef82",200 "relationship_type": "related-to",201 "start_time": "2022-06-13T10:33:33.000Z",202 "stop_time": 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"2022-06-13T07:35:22.000Z"1611 }1612 },1613 {1614 "node": {1615 "id": "03981894-96de-4c1a-9968-ccf75e87b0d1",1616 "relationship_type": "related-to",1617 "start_time": "2022-06-13T07:35:21.000Z",1618 "stop_time": "2022-06-13T07:35:21.000Z"1619 }1620 },1621 {1622 "node": {1623 "id": "7e8712d5-cd51-4331-9c03-1ffc3c136a5e",1624 "relationship_type": "related-to",1625 "start_time": "2022-06-13T07:35:20.000Z",1626 "stop_time": "2022-06-13T07:35:20.000Z"1627 }1628 }1629]1630/​/​ if (obj[199]) {1631/​/​ console.log('done')1632/​/​ }1633let dateTimeArray = obj.map((o,i) => {1634 if (obj[i+1]) {1635 return {1636 x: o['node'].start_time,1637 y: moment.utc(moment(o['node'].start_time)1638 .diff(moment(obj[i+1]['node'].start_time)))1639 .format("HH:mm:ss")1640 }1641 }1642})1643/​/​ console.log(dateTimeArray);1644const DATE_FORMATTER = 'DD/​MM/​YYYY';1645const DATE_TIME_FORMATTER = 'DD/​MM/​YYYY HH:mm:ss';1646function dateFormater(str) {1647 if (!str) {1648 return null;1649 }1650 /​* eslint-disable */​1651 const [dd, mm, yyyy] = str.split(/​(\-|\/​)/​g).filter((a) => {1652 /​/​ @ts-ignore1653 return !isNaN(a);1654 });1655 return `${+dd < 10 ? `0${+dd}` : dd}/​${+mm < 10 ? `0${+mm}` : mm}/​${yyyy}`;1656 /​* eslint-enable */​1657}1658function dateTimeFormatter(str) {1659 if (!str) {1660 return null;1661 }1662 const [dd, mm, yyyy, HH, MM, SS] = str.split(/​(\-|\/​|\s|\:)/​g).filter((a) => {1663 a = a.trim();1664 return a && !isNaN(a);1665 });1666 return `${+dd < 10 ? `0${+dd}` : dd}/​${+mm < 10 ? `0${+mm}` : mm}/​${yyyy} ${+HH < 10 ? `0${+HH}` : HH}:${+MM < 10 ? `0${+MM}` : MM}:${+SS < 10 ? `0${+SS}` : SS}`;1667 /​* eslint-enable */​1668}1669/​/​ console.log(dateTimeFormatter('01/​01/​2020 00-00-00'));1670/​/​ console.log(moment(dateTimeFormatter('12/​07/​2021 21/​36/​45'), DATE_TIME_FORMATTER, true).isValid());1671/​/​ console.log(moment('12/​07/​2021 21/​36/​45', DATE_TIME_FORMATTER, true).isValid());1672/​/​ console.log(moment('02/​31/​2021', DATE_FORMATTER, true).isValid())1673/​/​ console.log(moment('15/​01/​2021', DATE_FORMATTER, true).isValid())1674/​/​ console.log(moment().format('DD/​MM/​YYYY HH:mm:ss'));1675/​/​ console.log(moment('23/​08/​2022 13:26:15', 'DD/​MM/​YYYY HH:mm:ss', true).isValid());1676/​/​ console.log(moment('15/​01/​2021 13:21:46', 'YYYY-MM-DD HH:mm:ss', true).format('YYYY-MM-DD HH:mm:ss'));1677/​/​ const startFrom = moment('15/​07/​202', DATE_FORMATTER, true) > moment('15/​07/​2022', DATE_FORMATTER, true);1678/​/​ const isValid = moment(dateTimeFormatter('12/​08/​2022 16:25:41'), DATE_TIME_FORMATTER, true) >1679/​/​ moment(dateTimeFormatter('12/​08/​2022 13:25:41'), DATE_TIME_FORMATTER, true);1680/​/​ console.log(isValid);1681const convertSiteToUtcToday = (date) => {1682 if (date) {1683 return moment(date)1684 /​/​ .startOf('day')1685 .utc()1686 .format('YYYY-MM-DDTHH:mm:ss');1687 }1688 /​/​ return moment()1689 /​/​ .startOf('day')1690 /​/​ .utc()1691 /​/​ .format('YYYY-MM-DDTHH:mm:ss');1692};1693const convertUtcTodayToSite = (date) => {1694 return moment(date);1695};1696const createDateWithDateFormatter = (date) => {1697 return moment(date)1698 .startOf('day')1699 .utc()1700 .format('YYYY-MM-DDTHH:mm:ss');1701};1702/​/​ console.log(convertUtcTodayToSite('12/​08/​2022 18:25:41'));1703console.log(convertSiteToUtcToday('12/​08/​2022 12:25:41'));1704console.log(convertSiteToUtcToday('12/​08/​2022 13:56:24'));1705console.log(moment('2022-12-08T07:25:41'))1706console.log(moment.utc().format())1707console.log(moment().format())...

transcript_1.js

Source:transcript_1.js

1var transcript = {2 "results": [3 {4 "word_alternatives": [5 {6 "start_time": 0.8,7 "alternatives": [8 {9 "confidence": 1,10 "word": "my"11 }12 ],13 "end_time": 0.9614 },15 {16 "start_time": 0.96,17 "alternatives": [18 {19 "confidence": 1,20 "word": "family"21 }22 ],23 "end_time": 1.824 },25 {26 "start_time": 1.95,27 "alternatives": [28 {29 "confidence": 0.6107,30 "word": "absolute"31 },32 {33 "confidence": 0.1671,34 "word": "Apsley"35 },36 {37 "confidence": 0.069,38 "word": "outliers"39 },40 {41 "confidence": 0.0535,42 "word": "obsolete"43 },44 {45 "confidence": 0.0377,46 "word": "absolutes"47 },48 {49 "confidence": 0.0287,50 "word": "out"51 },52 {53 "confidence": 0.0188,54 "word": "outlays"55 }56 ],57 "end_time": 2.3858 },59 {60 "start_time": 2.39,61 "alternatives": [62 {63 "confidence": 0.6919,64 "word": "is"65 },66 {67 "confidence": 0.136,68 "word": "as"69 },70 {71 "confidence": 0.0287,72 "word": "please"73 }74 ],75 "end_time": 2.576 },77 {78 "start_time": 2.5,79 "alternatives": [80 {81 "confidence": 0.9897,82 "word": "fascinated"83 }84 ],85 "end_time": 3.5386 },87 {88 "start_time": 3.62,89 "alternatives": [90 {91 "confidence": 0.9992,92 "word": "by"93 }94 ],95 "end_time": 4.1196 },97 {98 "start_time": 4.14,99 "alternatives": [100 {101 "confidence": 0.9994,102 "word": "the"103 }104 ],105 "end_time": 4.23106 },107 {108 "start_time": 4.23,109 "alternatives": [110 {111 "confidence": 1,112 "word": "work"113 }114 ],115 "end_time": 4.58116 },117 {118 "start_time": 4.58,119 "alternatives": [120 {121 "confidence": 0.9743,122 "word": "I'm"123 },124 {125 "confidence": 0.0256,126 "word": "am"127 }128 ],129 "end_time": 4.79130 },131 {132 "start_time": 4.79,133 "alternatives": [134 {135 "confidence": 0.9999,136 "word": "doing"137 }138 ],139 "end_time": 5.06140 },141 {142 "start_time": 5.06,143 "alternatives": [144 {145 "confidence": 0.9996,146 "word": "with"147 }148 ],149 "end_time": 5.23150 },151 {152 "start_time": 5.23,153 "alternatives": [154 {155 "confidence": 0.9998,156 "word": "Watson"157 }158 ],159 "end_time": 5.87160 },161 {162 "start_time": 6.29,163 "alternatives": [164 {165 "confidence": 1,166 "word": "and"167 }168 ],169 "end_time": 6.8170 },171 {172 "start_time": 6.98,173 "alternatives": [174 {175 "confidence": 0.9833,176 "word": "I"177 },178 {179 "confidence": 0.0102,180 "word": "by"181 }182 ],183 "end_time": 7.13184 },185 {186 "start_time": 7.13,187 "alternatives": [188 {189 "confidence": 0.8472,190 "word": "I"191 }192 ],193 "end_time": 7.23194 },195 {196 "start_time": 7.23,197 "alternatives": [198 {199 "confidence": 0.9981,200 "word": "think"201 }202 ],203 "end_time": 7.43204 },205 {206 "start_time": 7.43,207 "alternatives": [208 {209 "confidence": 1,210 "word": "they"211 }212 ],213 "end_time": 7.52214 },215 {216 "start_time": 7.52,217 "alternatives": [218 {219 "confidence": 1,220 "word": "feel"221 }222 ],223 "end_time": 7.95224 },225 {226 "start_time": 7.98,227 "alternatives": [228 {229 "confidence": 1,230 "word": "a"231 }232 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],901 [902 "the",903 17.57,904 17.64905 ],906 [907 "world",908 17.64,909 18.08910 ]911 ],912 "confidence": 0.895,913 "transcript": "my family absolute is fascinated by the work I'm doing with Watson and I I think they feel a personal sense of pride and satisfaction about what the fact is not just technology for technology's sake is technology that's actually making a difference and then changing the world "914 }915 ],916 "final": true917 }918 ],919 "result_index": 0920 };921module.exports = {...

interval-partitioning.py

Source:interval-partitioning.py

1from collections import namedtuple, defaultdict2from operator import attrgetter3import heapq, functools, itertools, math4# ________________________________________________________________________________5# Topological sort and `heapindex` funtion.6class OrderableBunch(object):7 def __init__(self, **kwds):8 if 'key' not in kwds:9 raise ValueError("The *key* parameter is mandatory for ordering")10 11 self.__dict__.update(kwds)12 def __lt__(self, other):13 key = self.key14 return key(self) < key(other)15def topological_sort(graph, key_spec=(len, 0)):16 """Topological sort.17 >>> G = {18 ... 'v₁': set(),19 ... 'v₂': set(),20 ... 'v₃': {'v₂'},21 ... 'v₄': {'v₁','v₃'},22 ... 'v₅': {'v₁','v₂','v₃','v₄'},23 ... 'v₆': {'v₂','v₅'},24 ... 'v₇': {'v₁','v₅','v₆'}25 ... }26 >>> list(topological_sort(G))27 ['v₁', 'v₂', 'v₃', 'v₄', 'v₅', 'v₆', 'v₇']28 """29 key, check = key_spec # unpacking the spec for the priority rank function.30 q = [] # the priority queue.31 G = defaultdict(set) # The "usual" adjacency list representation of a graph;32 # btw, `set` is used as fallback ctor because of a fast33 # lookup in the forthcoming expression `children = G[node]`.34 for node, parents in graph.items():35 v = OrderableBunch(priority=key(parents), value=node, # the priority of each node depends on the rank of their `parents`. 36 key=attrgetter('priority')) # the newly OrderableBunch obj uses `priority` as key in the heapq.37 heapq.heappush(q, v) # push it into the queue mantaining the heap invariant.38 for parent in parents: # For each parent of `node`, the loop records39 G[parent].add(v) # this forward connection augmenting the graph `G`.40 while q:41 v = heapq.heappop(q) # It extracts the next value with higher priority42 assert v.priority == check # and it checks that its priority is consistent wrt `key`.43 node = v.value # Simple unpacking.44 yield node # A new record for the generator.45 children = G[node] # Fast lookup because G's values are `set` objects.46 if not children: continue # Noop.47 for child in children: # No need to use `heapindex` because we 48 child.priority -= 1 # reference OrderableBunch objs directly.49 50 heapq.heapify(q) # Restore the heap invariant in *linear time*.51def heapindex(q, item):52 """A generator of positions in which `item` occurs in `q`, in O(log n) time where n is `len(q)`.53 >>> import heapq54 >>> q = list(reversed(range(10)))55 >>> q56 [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]57 >>> heapq.heapify(q)58 >>> q59 [0, 1, 3, 2, 5, 4, 7, 9, 6, 8]60 >>> list(heapindex(q, 4))61 [5]62 >>> list(map(lambda item: min(heapindex(q, item)), q))63 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]64 >>> q = [1,3,3,3,10,10,2,2,4]65 >>> heapq.heapify(q)66 >>> q67 [1, 2, 2, 3, 10, 10, 3, 3, 4]68 >>> list(map(lambda item: list(heapindex(q, item)), [1,2,3,10,4]))69 [[0], [1, 2], [3, 7, 6], [4, 5], [8]]70 """71 L = len(q) # Simple upper bound for indexes.72 stack = [0] # Start with the position of the highest-priority obj.73 while stack: # Implement a recursive process by using a stack.74 k = stack.pop() # Handle the next position75 76 if k >= L or q[k] > item: # Outbound or greater than the desired item.77 continue78 79 if q[k] == item: # Good, remember `k` as a position where `item` lies.80 yield k81 stack.append(2*k+2) # According the the heapq's invariant, it proceeds82 stack.append(2*k+1) # in a logarithmic way.83# ________________________________________________________________________________84# Domain-specific Definitions.85job = namedtuple('job', ['start_time', 'duration', 'deadline', 'name']) 86dep = namedtuple('dep', ['name', 'jitter'])87def by(jobs, prop_name):88 return dict(zip(map(attrgetter(prop_name), jobs), jobs))89def finish_time(jb):90 return jb.start_time + jb.duration91def overlaps(I, J):92 return finish_time(I) > J.start_time # taking advantage of ordering93def ontime(J):94 return finish_time(J) <= J.deadline95def ordering(jobs, deps):96 deps_graph = {J.name: [d.name for d in deps[J.name]] for J in jobs}97 DAG = topological_sort(deps_graph)98 jobs_by_name = by(jobs, 'name')99 return [jobs_by_name[job_name] for job_name in DAG]100def run(graph, label, busy=defaultdict(list)):101 jobs, deps = graph # unpacking.102 def R(prefix, jobs, machine, label):103 if jobs: # still jobs to allocate.104 J_clean, *Js = jobs # unpacking.105 prefix_by_name = by(prefix, 'name')106 def ready_time(dp): # `dp` stands for `dependency`.107 D = prefix_by_name[dp.name]108 assert D.start_time is not None and D.name == dp.name109 rt = None110 if dp.jitter is None:111 rt = finish_time(D)112 else:113 assert dp.jitter > 0114 rt = D.start_time + dp.jitter115 return rt116 at_least = max(map(ready_time, deps[J_clean.name]), default=0)117 for st in itertools.count(max(at_least, J_clean.start_time or 0)):118 #min(J_clean.deadline - J_clean.duration,119 #max(map(attrgetter('duration'), jobs))) + 1):120 if J_clean.deadline is not math.inf and st + J_clean.duration > J_clean.deadline:121 break122 J = J_clean._replace(start_time=st)123 L = label[J.name].copy()124 for I in filter(functools.partial(overlaps, J=J), prefix): # O(n^2) complexity because of the last job; btw, preprocess of overlappings may help to check only those ones, getting a linear time.125 label[J.name] -= {machine[I.name]} # remove the machine on which job `I` is allocated for possibilities about job `J`.126 for l in label[J.name]:127 J_delayed = J128 for B in busy[l]:129 if overlaps(J_delayed, B):130 d = J_delayed.duration + B.duration131 J_delayed = J_delayed._replace(duration=d)132 else:133 break # assuming busy jobs are ordered too.134 if ontime(J_delayed):135 machine[J.name] = l # an attempt to allocate job `J` on machine `l`.136 yield from R([J_delayed] + prefix, Js, machine.copy(), label)137 label[J.name] = L138 else:139 assert len(machine) == len(prefix)140 assert all(map(lambda J: J.start_time is not None, prefix))141 yield (machine, prefix)142 return R([], jobs, {}, label.copy())143def sol_handler(sol):144 machine, prefix = sol145 M = {m: [] for m in set(machine.values())}146 for J in prefix:147 M[machine[J.name]].append(J)148 for k, v in M.items():149 v.sort()150 return M151def roassal(sol):152 return ['#({} {} {} {})'.format(machine, J.start_time, finish_time(J), J.name)153 for machine, jobs in sol.items() for J in jobs]154 155# ________________________________________________________________________________156# Problem instance157def liviotti():158 import random159 random.seed(1 << 5) # to reproduce the same values all the times.160 params = dict(required_jobs=50, max_duration=10, children_bounds=(5, 10), available_machines=10) # generation parameters.161 162 jobs = [job(start_time=None,163 duration=random.randint(1, params['max_duration']),164 deadline=math.inf, # for now every job can be allocated without 165 # time constraint, just schedule all of them.166 name=str(j))#chr(ord('A') + j)) 167 for j in range(params['required_jobs'])]168 deps = defaultdict(list)169 children = []170 for J in jobs:171 for c in range(random.randint(*params['children_bounds'])):172 C = J._replace(name=J.name + '_' + str(c), 173 duration=random.randint(1, params['max_duration']))174 children.append(C) # register `C` as a new job.175 deps[C.name] = [dep(name=J.name, jitter=None)] # `J` is parent of `C`176 J = C # `C` becomes the new parent for future children.177 jobs.extend(children)178 machines = set(map(str, range(params['available_machines']))) # at least each job goes to its machine.179 label = {J.name: machines.copy() for J in jobs} # each job can be assigned to any machine, initially.180 #label['A'] = {'M₃'} # job 'E' can be performed on the first machine only.181 #label['E'] = {'M₀'} # job 'E' can be performed on the first machine only.182 #label['D'] = {'M₁', 'M₂'} # job 'E' can be performed on the first machine only.183 """184 busy = defaultdict(list)185 busy.update({186 'M₀': [job(2, 3, None, 'cleaning'), 187 job(14, 1, None, 'sunday')],188 'M₁': [job(5, 2, None, 'maintenance')],189 })190 """191 busy = {machine: [job(i, 1, None, 'sunday') for i in range(7, 1000, 7)] 192 for machine in machines}193 print('Summary:\n=======\nJobs ({}): {}\nDeps: {}\n'.format(194 len(jobs), jobs, deps))195 196 sols = run((ordering(jobs, deps), deps), label.copy(), busy)197 print()198 for i, sol in zip(range(1), map(sol_handler, sols)):199 #for sol in map(sol_handler, sols):200 print('#({})'.format(' '.join(roassal(sol))), '\n')201 202def simple_test():203 """204 >>> jobs = [job(None, 3, 3, 'A')]205 >>> deps = defaultdict(list)206 >>> sols = run((ordering(jobs, deps), deps), {'A':{'M₀'}})207 >>> list(map(sol_handler, sols))208 [{'M₀': [job(start_time=0, duration=3, deadline=3, name='A')]}]209 >>> jobs = [job(1, 3, 6, 'A')] # if we put 8 as a deadline we should obtain allocations upto 4.210 >>> sols = run((ordering(jobs, deps), deps), {'A':{'M₀'}})211 >>> list(map(sol_handler, sols)) # doctest: +NORMALIZE_WHITESPACE212 [{'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 213 {'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 214 {'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}]215 >>> jobs.append(job(None, 3, 10, 'B'))216 >>> deps['B'] = [dep(name='A', jitter=None)]217 >>> sols = run((ordering(jobs, deps), deps), {'A':{'M₀'}, 'B':{'M₀', 'M₁'}})218 >>> list(sorted(map(sol_handler, sols), key=len)) # doctest: +NORMALIZE_WHITESPACE219 [{'M₀': [job(start_time=1, duration=3, deadline=6, name='A'), 220 job(start_time=4, duration=3, deadline=10, name='B')]}, 221 {'M₀': [job(start_time=1, duration=3, deadline=6, name='A'), 222 job(start_time=5, duration=3, deadline=10, name='B')]}, 223 {'M₀': [job(start_time=1, duration=3, deadline=6, name='A'), 224 job(start_time=6, duration=3, deadline=10, name='B')]}, 225 {'M₀': [job(start_time=1, duration=3, deadline=6, name='A'), 226 job(start_time=7, duration=3, deadline=10, name='B')]}, 227 {'M₀': [job(start_time=2, duration=3, deadline=6, name='A'), 228 job(start_time=5, duration=3, deadline=10, name='B')]}, 229 {'M₀': [job(start_time=2, duration=3, deadline=6, name='A'), 230 job(start_time=6, duration=3, deadline=10, name='B')]}, 231 {'M₀': [job(start_time=2, duration=3, deadline=6, name='A'), 232 job(start_time=7, duration=3, deadline=10, name='B')]}, 233 {'M₀': [job(start_time=3, duration=3, deadline=6, name='A'), 234 job(start_time=6, duration=3, deadline=10, name='B')]}, 235 {'M₀': [job(start_time=3, duration=3, deadline=6, name='A'), 236 job(start_time=7, duration=3, deadline=10, name='B')]}, 237 {'M₁': [job(start_time=4, duration=3, deadline=10, name='B')], 238 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 239 {'M₁': [job(start_time=5, duration=3, deadline=10, name='B')], 240 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 241 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 242 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 243 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 244 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 245 {'M₁': [job(start_time=5, duration=3, deadline=10, name='B')], 246 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 247 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 248 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 249 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 250 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 251 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 252 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}, 253 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 254 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}]255 >>> deps['B'] = [dep(name='A', jitter=1)]256 >>> sols = run((ordering(jobs, deps), deps), {'A':{'M₀'}, 'B':{'M₁'}})257 >>> list(sorted(map(sol_handler, sols), key=len)) # doctest: +NORMALIZE_WHITESPACE258 [{'M₁': [job(start_time=2, duration=3, deadline=10, name='B')], 259 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 260 {'M₁': [job(start_time=3, duration=3, deadline=10, name='B')], 261 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 262 {'M₁': [job(start_time=4, duration=3, deadline=10, name='B')], 263 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 264 {'M₁': [job(start_time=5, duration=3, deadline=10, name='B')], 265 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 266 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 267 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 268 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 269 'M₀': [job(start_time=1, duration=3, deadline=6, name='A')]}, 270 {'M₁': [job(start_time=3, duration=3, deadline=10, name='B')], 271 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 272 {'M₁': [job(start_time=4, duration=3, deadline=10, name='B')], 273 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 274 {'M₁': [job(start_time=5, duration=3, deadline=10, name='B')], 275 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 276 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 277 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 278 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 279 'M₀': [job(start_time=2, duration=3, deadline=6, name='A')]}, 280 {'M₁': [job(start_time=4, duration=3, deadline=10, name='B')], 281 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}, 282 {'M₁': [job(start_time=5, duration=3, deadline=10, name='B')], 283 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}, 284 {'M₁': [job(start_time=6, duration=3, deadline=10, name='B')], 285 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}, 286 {'M₁': [job(start_time=7, duration=3, deadline=10, name='B')], 287 'M₀': [job(start_time=3, duration=3, deadline=6, name='A')]}]288 """289 pass...

benchmark_time.py

Source:benchmark_time.py

1import tensorflow as tf2import numpy as np3import pandas as pd4import time5from tqdm import tqdm6from sklearn.model_selection import train_test_split7from scipy.stats import pearsonr8from contextual_decomposition import ContextualDecompositionExplainerTF9from gradients import GradientExplainerTF10from neural_interaction_detection import NeuralInteractionDetectionExplainerTF11from path_explain import PathExplainerTF, softplus_activation12from shapley_sampling import SamplingExplainerTF13def build_model(num_features,14 units=[128, 128],15 activation_function=tf.keras.activations.softplus,16 output_units=1):17 model = tf.keras.models.Sequential()18 model.add(tf.keras.layers.Input(shape=(num_features,)))19 for unit in units:20 model.add(tf.keras.layers.Dense(unit))21 model.add(tf.keras.layers.Activation(activation_function))22 model.add(tf.keras.layers.Dense(output_units))23 return model24def get_data(num_samples,25 num_features):26 x = np.random.randn(num_samples, num_features).astype(np.float32)27 return x28def benchmark_time():29 number_of_layers = [5]30 number_of_samples = [1000]31 number_of_features = [5, 50, 500]32 layer_array = []33 sample_array = []34 feature_array = []35 time_dict = {}36 for method in ['ih', 'eh', 'cd', 'nid', 'hess', 'hess_in', 'sii_sampling', 'sii_brute_force']:37 for eval_type in ['all', 'row', 'pair']:38 time_dict[method + '_' + eval_type] = []39 for layer_count in number_of_layers:40 for sample_count in number_of_samples:41 for feature_count in number_of_features:42 print('Number of layers: {} - Number of samples: {} - Number of features: {}'.format(layer_count, sample_count, feature_count))43 model = build_model(num_features=feature_count,44 activation_function=softplus_activation(beta=10.0))45 data = get_data(sample_count, feature_count)46 ###### Shapley Interaction Index Brute Force ######47 sii_explainer = SamplingExplainerTF(model)48 print('Shapley Interaction Index Brute Force')49 if feature_count < 10:50 start_time = time.time()51 _ = sii_explainer.interactions(inputs=data,52 baselines=np.zeros(feature_count).astype(np.float32),53 batch_size=100,54 output_index=0,55 feature_index=None,56 number_of_samples=None,57 verbose=True)58 end_time = time.time()59 time_dict['sii_brute_force_all'].append(end_time - start_time)60 start_time = time.time()61 for i in tqdm(range(1, feature_count)):62 _ = sii_explainer.interactions(inputs=data,63 baselines=np.zeros(feature_count).astype(np.float32),64 batch_size=100,65 output_index=0,66 feature_index=(0, i),67 number_of_samples=None)68 end_time = time.time()69 time_dict['sii_brute_force_row'].append(end_time - start_time)70 start_time = time.time()71 _ = sii_explainer.interactions(inputs=data,72 baselines=np.zeros(feature_count).astype(np.float32),73 batch_size=100,74 output_index=0,75 feature_index=(0, 1),76 number_of_samples=None,77 verbose=True)78 end_time = time.time()79 time_dict['sii_brute_force_pair'].append(end_time - start_time)80 else:81 time_dict['sii_brute_force_all'].append(np.nan)82 time_dict['sii_brute_force_row'].append(np.nan)83 time_dict['sii_brute_force_pair'].append(np.nan)84 ###### Shapley Interaction Index Sampling ######85 print('Shapley Interaction Index Sampling')86 if feature_count < 100:87 start_time = time.time()88 _ = sii_explainer.interactions(inputs=data,89 baselines=np.zeros(feature_count).astype(np.float32),90 batch_size=100,91 output_index=0,92 feature_index=None,93 number_of_samples=200,94 verbose=True)95 end_time = time.time()96 time_dict['sii_sampling_all'].append(end_time - start_time)97 else:98 time_dict['sii_sampling_all'].append(np.nan)99 start_time = time.time()100 for i in tqdm(range(1, feature_count)):101 _ = sii_explainer.interactions(inputs=data,102 baselines=np.zeros(feature_count).astype(np.float32),103 batch_size=100,104 output_index=0,105 feature_index=(0, i),106 number_of_samples=200)107 end_time = time.time()108 time_dict['sii_sampling_row'].append(end_time - start_time)109 start_time = time.time()110 _ = sii_explainer.interactions(inputs=data,111 baselines=np.zeros(feature_count).astype(np.float32),112 batch_size=100,113 output_index=0,114 feature_index=(0, 1),115 number_of_samples=200,116 verbose=True)117 end_time = time.time()118 time_dict['sii_sampling_pair'].append(end_time - start_time)119 ###### Integrated and Expected Hessians ######120 print('Integrated Hessians')121 path_explainer = PathExplainerTF(model)122 start_time = time.time()123 _ = path_explainer.interactions(inputs=data,124 baseline=np.zeros((1, feature_count)).astype(np.float32),125 batch_size=100,126 num_samples=200,127 use_expectation=False,128 output_indices=0,129 verbose=True,130 interaction_index=None)131 end_time = time.time()132 time_dict['ih_all'].append(end_time - start_time)133 start_time = time.time()134 _ = path_explainer.interactions(inputs=data,135 baseline=np.zeros((1, feature_count)).astype(np.float32),136 batch_size=100,137 num_samples=200,138 use_expectation=False,139 output_indices=0,140 verbose=True,141 interaction_index=0)142 end_time = time.time()143 time_dict['ih_row'].append(end_time - start_time)144 time_dict['ih_pair'].append(end_time - start_time)145 print('Expected Hessians')146 start_time = time.time()147 _ = path_explainer.interactions(inputs=data,148 baseline=np.zeros((200, feature_count)).astype(np.float32),149 batch_size=100,150 num_samples=200,151 use_expectation=True,152 output_indices=0,153 verbose=True,154 interaction_index=None)155 end_time = time.time()156 time_dict['eh_all'].append(end_time - start_time)157 start_time = time.time()158 ih_interactions = path_explainer.interactions(inputs=data,159 baseline=np.zeros((200, feature_count)).astype(np.float32),160 batch_size=100,161 num_samples=200,162 use_expectation=True,163 output_indices=0,164 verbose=True,165 interaction_index=0)166 end_time = time.time()167 time_dict['eh_row'].append(end_time - start_time)168 time_dict['eh_pair'].append(end_time - start_time)169 ###### Contextual Decomposition ######170 print('Contextual Decomposition')171 cd_explainer = ContextualDecompositionExplainerTF(model)172 start_time = time.time()173 _ = cd_explainer.interactions(inputs=data,174 batch_size=100,175 output_indices=0,176 interaction_index=None)177 end_time = time.time()178 time_dict['cd_all'].append(end_time - start_time)179 start_time = time.time()180 _ = cd_explainer.interactions(inputs=data,181 batch_size=100,182 output_indices=0,183 interaction_index=0)184 end_time = time.time()185 time_dict['cd_row'].append(end_time - start_time)186 start_time = time.time()187 _ = cd_explainer.interactions(inputs=data,188 batch_size=100,189 output_indices=0,190 interaction_index=(0, 1))191 end_time = time.time()192 time_dict['cd_pair'].append(end_time - start_time)193 ###### Neural Interaction Detection ######194 print('Neural Interaction Detection')195 nid_explainer = NeuralInteractionDetectionExplainerTF(model)196 start_time = time.time()197 _ = nid_explainer.interactions(output_index=0,198 verbose=True,199 inputs=data,200 batch_size=100)201 end_time = time.time()202 time_dict['nid_all'].append(end_time - start_time)203 start_time = time.time()204 _ = nid_explainer.interactions(output_index=0,205 verbose=True,206 inputs=data,207 batch_size=100,208 interaction_index=0)209 end_time = time.time()210 time_dict['nid_row'].append(end_time - start_time)211 start_time = time.time()212 _ = nid_explainer.interactions(output_index=0,213 verbose=True,214 inputs=data,215 batch_size=100,216 interaction_index=(0, 1))217 end_time = time.time()218 time_dict['nid_pair'].append(end_time - start_time)219 ###### Input Hessian ######220 print('Input Hessian')221 grad_explainer = GradientExplainerTF(model)222 start_time = time.time()223 hess_interactions = grad_explainer.interactions(inputs=data,224 multiply_by_input=False,225 batch_size=100,226 output_index=0)227 end_time = time.time()228 time_dict['hess_all'].append(end_time - start_time)229 start_time = time.time()230 hess_interactions = grad_explainer.interactions(inputs=data,231 multiply_by_input=False,232 batch_size=100,233 output_index=0,234 interaction_index=0)235 end_time = time.time()236 time_dict['hess_row'].append(end_time - start_time)237 time_dict['hess_pair'].append(end_time - start_time)238 start_time = time.time()239 hess_interactions = grad_explainer.interactions(inputs=data,240 multiply_by_input=True,241 batch_size=100,242 output_index=0)243 end_time = time.time()244 time_dict['hess_in_all'].append(end_time - start_time)245 start_time = time.time()246 hess_interactions = grad_explainer.interactions(inputs=data,247 multiply_by_input=True,248 batch_size=100,249 output_index=0,250 interaction_index=0)251 end_time = time.time()252 time_dict['hess_in_row'].append(end_time - start_time)253 time_dict['hess_in_pair'].append(end_time - start_time)254 layer_array.append(layer_count)255 sample_array.append(sample_count)256 feature_array.append(feature_count)257 time_dict['hidden_layers'] = layer_array258 time_dict['number_of_samples'] = sample_array259 time_dict['number_of_features'] = feature_array260 time_df = pd.DataFrame(time_dict)261 time_df.to_csv('time.csv', index=False)262if __name__ == '__main__':263 tf.autograph.set_verbosity(0)...

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