├── README.md ├── data ├── hawkes │ ├── time-test.txt │ └── time-train.txt └── self-correcting │ ├── event-test.txt │ ├── event-train.txt │ ├── time-test.txt │ ├── time-train.txt │ └── time.txt ├── model.py ├── test.py └── train.py /README.md: -------------------------------------------------------------------------------- 1 | # RMTPP-pytorch 2 | ## Material in this file: 3 | Introduction 4 | Preparation and How to Run Code 5 | Model Description 6 | Result 7 | Acknowledgement and Reference 8 | ### Introduction 9 | This repository is the implementation of model described in paper Du, Nan, et al. [“Recurrent Marked Temporal Point Processes.”](https://www.kdd.org/kdd2016/subtopic/view/recurrent-temporal-point-process) by pytorch. This repository also use the same data as the data used in the paper. The rest of this section is a recap of the paper. 10 | 11 | Discrete events with different types, which is defined as Marked Temporal Point Process, is usually generated in real life setting. Some past events may increase or decrease the occurrence probability of other kinds of event in the future. For example, in health care system having some type of medicines may cause a lower probability of some specific disease, and in social media like twitter some tweets may cause an increasing number of tweets of the same topic in a short time. The Recurrent Marked Temporal Point Process (RMTPP) model is robust to predict the next event time and next event type. That is, given a stream of past event times t and past event types k of form 
{(k1, t1), (k2, t2), … (kn, tn)}, where 1,2,n represents the 1st, 2nd, nth event
, it can predict the next type and type 
(kn+1, tn+1)
12 | 13 | ### Preparation and How to Run the Code 14 | You can run the code on your own computer or on python notebooks like Google Colab, Anaconda Jupyter Notebook. It is recommended to run the script on google colab (https://colab.research.google.com/) or similar python notebooks if you do not have a dedicated GPU on your computer. 15 | 16 | 1. To run the program on your computer, please make sure that you have the following files and packages being downloaded.
17 | - Python3: you can download through the link here: https://www.python.org/ 18 | - Numpy: you can dowload it through command 
pip install numpy
19 | - Scikit-Learn: you can download it through command 
pip install sklearn
20 | - Scipy: you can download it through command 
pip install scipy
21 | - Pytorch: pytorch installation is more complicated than the package described above. You can go to https://pytorch.org/get-started/locally/ for more information. If you still cannot install it on windows computer through pip, you can download Anaconda first and then download the pytorch through method described here: https://dziganto.github.io/data%20science/python/anaconda/Creating-Conda-Environments/

22 | 23 | 24 | 2. You can also run the code on Google Colab. You first need to connect this repository to Colab by the following code:
!git clone https://github.com/Hongrui24/RMTPP-pytorch
Then cd to this directory by 
!cd RMTPP-pytorch
25 | 26 | 3. To train the model, you can type the following for guide: 
!python train.py --help
Sample command line can be 
!python train.py --lr 0.03 --epochs 500 --data hawkes

27 | To test the trained model, you can type the following for guide: 
!python test.py --help
Sample command line can be 
!python test.py --data hawkes
28 | 29 | After done the test, you can find the result plots in the same file named by "result.png". 30 | 31 | ### Model Description 32 | This is a recap of the model described in the paper: 33 | 34 | This model is trained by Backpropogation Through Time (BPTT) method. That is, for each event stream {(kj, tj)} we take b consecutive events as input into the model, and unroll the model for b steps through time. However, we will use the whole history of event stream to make prediction for hawkes and self-correcting. 35 | We input the jth event (kj, tj) into the jth layer of the model. The event type, which is in one-hot representation, is embeded into a single value. 
 yj = We·kj+b
We extract the inter-event duration from the time tj by making 
 dj = tj - tj-1, and d0 = 0
Then, we feed the type value and inter-event duration into the jth layer of the RNN cell. In order to prevent the dying relu problem, all weights are initialized with positive values. We update the hidden layer of the RNN by 
 hj = relu(Wy·yj + Wt·dj+Wh·hj-1 + b)
Then, we get the predict event type probability through softmax method. We can also get the time intensity and conditional density of time by 
λ*(t) = exp(V·ht + w*dt + bias)
f(t) = exp(V·ht + w*dt + bias + (exp(V·ht+ bias)-exp(V·ht + w*dt + bias)/w)
For training process, we minimize the negative log likelihood of the time, and we use the expectation to predict the next time. 36 | 37 | 38 | 39 | ### Model Test Results 40 | 41 | We test our model with the data used in the paper's generative experiments of predicting Hawkes and Self-correcting. The results show that the model implemented by pytorch is able to get the similar result as the result described in the paper after the model is trained with 500 epochs with 0.03 learning rate. In each of the picture below the graphs are prediction on inter-event duration, intensity, and Root-Mean-Squared-Error accordingly.
42 | Test on Hawkes:
43 | ![Hawkes](https://user-images.githubusercontent.com/54515153/84570792-a630c800-ad5d-11ea-972e-a809f0865add.png)
44 | Comparing to the results on the paper: 45 | ![Hawkes](https://user-images.githubusercontent.com/54515153/85622693-3c55cf80-b635-11ea-96c9-3d43b0526556.JPG)
46 |

47 | Test on Self-correcting:
48 | ![self-correcting](https://user-images.githubusercontent.com/54515153/84570795-a9c44f00-ad5d-11ea-9b71-30632793f9b4.png)
49 | Comparing to the results on the paper: 50 | ![Self-correcting](https://user-images.githubusercontent.com/54515153/85622697-3d86fc80-b635-11ea-99f1-d6d5835e642c.JPG) 51 | 52 | 53 | ### Acknowledgement 54 | The model is built by Hongrui Lyu, supervised by of Hyunouk Ko and Xiaoming Huo. This repository is built upon the model described in paper Du, Nan, et al. [“Recurrent Marked Temporal Point Processes.”](https://www.kdd.org/kdd2016/subtopic/view/recurrent-temporal-point-process). We also use a similar pytorch implementation by [Yunxuan Xiao](https://github.com/woshiyyya/ERPP-RMTPP) to debug the model. 55 | -------------------------------------------------------------------------------- /data/hawkes/time-test.txt: -------------------------------------------------------------------------------- 1 | 0.105662161187 0.217422690787 0.63849237767 0.752145532084 1.26323942152 1.7093075963 1.80650402695 2.84978593604 3.54519792764 4.01070158484 6.89714908912 7.46334052693 8.37979673604 8.48972978016 9.72531651704 14.4385452832 15.6682542832 17.3708123822 24.3356321178 26.0835240249 26.648959334 27.0667663922 54.6793834202 58.3929905523 62.4370055119 62.6811261513 62.9241957243 63.1505151223 63.1849816441 63.8640251102 64.018903277 64.3545249108 65.1331191366 66.4926630794 69.8856661593 70.5824156834 80.4656646839 80.594800601 80.9393995184 81.0400447211 81.0546781498 81.0815105656 81.7592009265 83.1556399387 83.3840824831 84.127902511 84.3965077619 85.3557564401 86.9378458526 87.0966189067 87.5231999671 88.9630047392 91.326198849 91.4408654179 92.0798593696 93.5894540611 94.6312965509 94.6587690452 96.0572114084 100.683356604 101.239327312 101.836002805 102.095157611 105.412524631 106.013333344 107.76450582 111.262684241 114.120225661 123.796737587 123.821975939 126.294056326 126.647252838 126.997734456 128.029278147 128.93288659 129.589192222 130.081170998 131.070900491 131.464077222 131.850939009 133.417855568 137.943029097 141.313761342 152.849339341 152.996341893 153.258562347 153.393727848 153.530730618 155.539073886 155.540554227 170.203868854 172.09955041 172.188435516 172.986499406 173.027270859 174.473687797 175.953629009 176.021579793 176.295157657 177.473671152 178.640009993 178.673645863 178.768655516 178.788512461 179.089850824 179.538540079 179.690551662 179.799261593 179.999351675 180.109013581 180.276572429 180.279261487 180.295784254 180.30386534 180.326720064 180.372162473 180.420244258 180.442648876 180.483300927 180.660600609 180.785488668 180.836578184 180.881032387 180.967410178 180.97475331 181.016858893 181.076066207 181.216834234 181.22785736 181.253035215 181.340687517 181.442107744 181.568197206 181.607685711 181.612590492 181.63665972 181.690037269 181.697998422 181.714128843 181.855731274 181.874778782 182.168705566 182.282602536 182.458336887 182.581990703 182.99754447 183.02730543 183.062810724 183.066047922 183.298071671 183.408008949 183.621963612 183.680062527 183.739890812 183.881419515 184.117757324 2 | 0.903059694496 1.25177901977 1.94700816227 2.92921653764 2.94511035423 3.63765774192 3.91711834067 4.11378649603 4.79998985485 5.01034196519 5.36768875411 6.28703125568 7.32833380214 9.6327130714 18.5995362483 20.4966692092 22.3007183094 22.6412702363 30.6888133896 30.935136911 33.0595505638 51.8927556166 52.2581704334 52.2932331852 54.1340085112 55.0480096008 55.7331512569 62.6806721433 62.7965209 64.0703688564 64.6857519746 75.4967215065 76.4208137799 76.5887816274 77.4472643141 77.6579958657 77.7178395427 77.9887084924 78.1346810042 79.0724104228 79.3400554815 79.4457234617 80.0679429733 80.1075210217 80.1149316257 80.1784030874 80.3487797577 80.8375687831 81.1228243343 82.7756446662 83.0880363931 83.1810347899 83.2194027387 84.4497323659 86.0485406301 86.1258022332 86.2321764844 87.3433621516 87.6772697334 87.7052421282 87.7091133917 88.4255462523 88.7522308194 89.392556334 89.4756751318 89.5904333209 89.6598006392 89.6818925229 89.851892354 89.9133598917 89.9393207678 90.7631557472 90.9414832264 91.0718014536 91.2035866651 91.2711893078 91.9132987729 92.3406186941 92.7595603008 92.7655716835 94.7414059836 94.9194623354 95.1681372694 118.57754673 124.2611173 128.164167043 137.052856412 146.597147114 147.605419062 155.306996642 156.703579133 156.792064376 157.63330084 158.40771502 159.224033264 160.398500232 160.811260985 163.422421076 167.467988414 179.22699791 179.799457303 180.270774655 180.552514425 181.231477436 181.543617815 181.544777706 181.702090887 181.852567894 182.099768675 182.702179748 183.468761689 185.466244245 198.772724553 199.075255085 200.586047774 201.658657217 203.115470971 204.292043643 222.724891373 226.06154161 226.129102698 226.131365184 227.594954474 227.806352269 228.07754028 228.126981354 229.203418333 229.660926917 229.726093292 230.250042967 230.432135471 231.441777628 231.450995052 233.453453924 242.863911821 243.156534601 243.712560001 244.442587553 244.629501786 245.64198852 245.656640843 247.71103544 261.983749591 263.407962666 263.786839606 263.984313199 269.371787066 269.401726201 269.566189321 269.672186054 269.836388105 270.446288039 271.78207571 271.885365604 272.125939054 272.276658853 3 | 0.0182807870769 0.273735270465 0.649932153862 0.974648591933 1.71636302414 1.94681536735 4.21649115275 5.03657445107 5.74235413855 5.90465270483 6.39280163283 6.46946104682 6.50412738125 6.5387180982 6.66530755392 6.66953838932 6.72953177563 6.80787194686 7.02237764234 7.12127872618 7.14468693553 7.20322738608 7.23193489953 7.29292119387 7.3090746125 7.33898404088 7.49587106658 7.61312699555 7.62438782385 7.65854636622 7.77890551164 7.87185666878 8.332584191 8.54826177677 8.58135340523 8.6452370365 8.65852752818 8.77107861468 8.8381016719 8.86737851032 8.8839696212 9.00962635307 9.09927702462 9.16453327434 9.20471127615 9.30021036285 9.35641424501 9.38120226451 9.54133258176 9.54512829691 9.54541938319 9.66760017043 9.73562360462 9.87084094221 9.91971011061 9.97131627863 10.0787879707 10.0882626066 10.1908405199 10.3056921316 10.4084939633 10.4613702727 10.5658008629 10.5761339519 10.7027998036 10.7668393677 11.0790777032 11.1194202255 11.1861570939 11.3734002954 11.5294543929 11.5360482119 11.7228424537 11.99904298 12.56019615 12.6266137791 12.8219438246 12.882761378 12.9033675131 12.9743000585 13.0281474522 13.1754662065 13.2346651669 13.4415539078 13.5656422118 13.6584354777 13.8446752686 14.1515092759 14.2683667167 14.2840665708 14.3177271262 14.7779620041 15.1934933912 15.2331762499 15.29951156 15.6621629461 15.6921146885 16.9436753011 20.0966012683 24.8142501171 28.4436438215 31.8878060924 32.3395346023 36.4981942775 38.2259968819 38.3540014644 38.5940675092 38.6228133528 38.8937394774 39.0131715257 39.0151282941 39.1200956167 39.379024323 39.6009184534 39.8831714046 39.9691442855 40.0014528496 40.1267242956 40.2881685395 40.3290899284 40.4378726127 40.5870616809 41.7078226018 41.7696768724 44.0567336779 44.3511666077 53.6268677825 53.764910722 53.8813520369 53.9172651175 55.1218863539 57.0550547291 58.4448506024 58.6746422071 59.0147368749 67.3660835236 69.7879098004 72.6126312275 72.9404924466 73.9850730868 74.3463651342 74.5220244684 74.9509645444 75.0958395634 75.1645492596 75.5682858904 76.0275387611 76.1620497529 76.1687940722 76.2187334757 76.378731939 76.4371145483 76.4517601936 76.5165276308 76.5536640683 76.5683609861 4 | 0.00439912806178 0.26944748635 0.324754341154 0.441355314139 0.686891522806 0.709318836265 1.27455593948 2.72092781144 3.35173492459 3.387435204 4.32975511345 5.26641150237 5.68344504522 9.60809587482 10.7294040955 10.9448317273 11.3282591405 11.7190678991 40.1267982903 45.3403490271 52.3539516719 55.8213935284 56.8956330808 58.7712531403 60.8353225411 60.8923451918 60.9028484668 61.2798425744 61.609085166 61.8073906783 61.865815996 63.2150529244 63.601971342 66.3637850405 66.5288060626 67.8729732383 70.3412306291 74.787078259 75.2834484218 75.859289337 78.4127146662 80.0442631761 80.7661478908 80.8344975349 81.0658997067 81.5761139576 81.6009912262 81.6720366968 81.7791582626 82.385627892 82.6329309793 82.64016926 82.8796328821 82.8934487347 83.1978053729 83.5246769054 83.5871463312 84.1752939364 84.4620253076 84.5269550574 84.8134717015 89.3322839595 90.4393115118 90.4807695492 91.3339588992 91.4362279635 91.5979898252 91.7769044771 93.5053771903 93.9171281959 94.6996523334 95.4610287454 96.770827638 100.13968181 100.605511503 100.864028642 103.91201848 131.274398385 134.094102404 138.743179223 138.935516848 139.55893031 144.817244202 144.94383876 149.58061754 151.127162401 152.872759439 152.891422712 153.21143309 153.569821173 153.826670738 153.838879614 154.243560578 154.490015619 159.743121266 159.928649693 160.211886926 162.266033879 162.908094682 163.108842584 164.18195557 164.657279448 164.920007733 165.230959776 165.448601164 166.289336761 167.40880938 175.580028005 180.507652667 180.810780554 180.947219502 181.667805347 182.333729829 184.612931492 185.346147517 185.459992857 185.751076729 186.101028555 186.4225684 188.080134133 188.457866153 189.386176508 190.152697568 192.719619071 194.273477594 194.41173482 194.614667932 196.523549998 197.072137559 197.254751352 197.856718148 199.258166029 199.437632915 199.473097499 200.481181153 201.248237461 201.60331549 201.739928577 203.5781875 203.729479332 207.114166386 207.186927672 207.721449859 208.058772868 208.292484148 208.837168708 209.234520785 212.887437085 213.999023553 214.101978568 214.42936412 214.872130085 216.73804371 217.099658529 219.502751664 219.68390134 5 | 0.649191328877 1.70784210887 5.60567593979 7.93380206867 8.20726568885 8.27365346868 8.74899377501 9.14362262626 9.71610876639 9.82264704399 9.92568889881 10.4220681531 16.1053097283 22.3178904015 22.8540711759 25.5425146786 25.716287489 26.3565708599 28.2177010501 31.9038310241 33.1005551923 34.4735780494 34.9874233262 36.5780986526 36.7988170765 38.1157322617 38.2843313365 38.3998314885 38.652512064 39.040457759 39.3135671813 39.4584055752 39.6335145534 39.9041767681 39.9559635973 40.7506276398 41.5586752411 43.1624812561 44.1486668968 45.0021065447 47.8680649055 50.6483722858 50.9430099195 56.2931162119 61.636306213 61.7154570692 63.397835605 63.449807173 63.7048831352 77.7138253814 77.9478368611 85.7369391211 93.0396779867 97.9495300676 98.9204591335 99.7685136434 100.198094723 103.068991291 103.079037962 104.19464788 110.548980173 113.502357372 125.035256954 126.8603519 127.635581444 127.990140723 128.898410833 128.951194796 129.078790095 129.967365311 130.00601248 130.076805432 130.798024926 130.92743325 131.139987277 131.560347136 132.368193063 133.747823387 134.902314297 135.500910645 136.338621896 136.573609301 136.676353553 137.49033548 138.236000412 138.277500947 138.540629979 139.879141631 139.956107662 140.324786491 140.333251794 140.417639719 140.443080253 140.624825324 140.696603084 140.865808076 141.20747293 141.409331563 141.507166638 141.697797163 141.805477171 142.036999875 142.184549145 142.284467877 142.470656687 143.020172186 143.28281094 143.305537624 143.326928407 143.484001765 143.507697859 143.53736502 143.87004491 144.02069657 144.073681596 144.61264059 144.764943437 145.096365862 145.103812836 146.344478501 146.492716516 147.078616162 147.114058481 148.31799063 149.347402452 149.550742599 149.996431807 150.306672581 150.812765085 151.164408226 151.592791316 152.41356538 152.464922059 152.667361696 152.799627497 152.873699331 153.53759953 153.720819085 154.292760522 154.814493262 154.816980741 155.345825873 155.360288661 155.431323753 156.267157248 156.332429746 156.593647028 156.701329102 156.701774484 156.766076047 156.786213289 156.867061683 156.986935744 157.421839912 158.226966184 158.435332781 6 | 0.19237101318 0.369500482031 1.51229307219 2.86517414745 2.98771977216 3.07301632337 3.09736523086 3.16786221564 3.41275195831 3.94887893325 4.32253317649 4.37984124687 5.75704428512 35.3220987063 35.3466155698 47.588289396 48.0330354151 48.4352491153 48.5655407914 48.8346449759 48.8962836452 49.4321405862 49.9304131075 50.2991431909 50.957444026 50.9718694365 51.7265421651 52.0339189663 53.0966899318 53.3403922591 53.5494245022 54.6357424022 55.0343802621 55.0584520622 55.1791262354 55.4958995651 55.6214147492 55.9412933376 56.4331812504 56.5758400729 56.6007163471 56.6415637384 56.9717722468 57.2197047488 57.52630928 57.5374027486 57.7654101968 57.9058077959 57.9471090371 59.1404262708 59.5097956651 60.3655364381 60.917925195 63.5255750144 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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 54 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 55 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 56 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 57 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 58 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 59 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 60 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 61 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 62 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 63 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 64 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 65 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class RMTPP(nn.Module): 6 | def __init__(self, config, device): 7 | super(RMTPP, self).__init__() 8 | self.config = config 9 | self.n_class = config.n_class 10 | self.hid_dim = config.hid_dim 11 | self.embed = nn.Linear(in_features=self.n_class, out_features=1) 12 | self.rnn = nn.RNN(input_size=2, hidden_size=config.hid_dim, 13 | batch_first=True, num_layers=config.n_layers,bidirectional=False, nonlinearity='relu') 14 | 15 | self.event_linear = nn.Linear(in_features=config.hid_dim, out_features=self.n_class, bias=True) 16 | self.time_linear = nn.Linear(in_features=config.hid_dim, out_features=1, bias=True) 17 | 18 | self.event_criterion = nn.CrossEntropyLoss() 19 | self.intensity_w = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) 20 | self.intensity_b = nn.Parameter(torch.tensor(0.1, dtype=torch.float32)) 21 | self.time_criterion = self.RMTPPLoss 22 | self.optimizer = self.set_optimizer(config.opt, config.lr, config.momentum) 23 | 24 | def RMTPPLoss(self, hidden_things, time_duration): 25 | loss = torch.mean(hidden_things+self.intensity_w * time_duration + self.intensity_b + 26 | (torch.exp(hidden_things + self.intensity_b) - 27 | torch.exp(hidden_things+self.intensity_w * time_duration + self.intensity_b))/self.intensity_w) 28 | return -loss 29 | 30 | def forward(self, time_input, event_input): 31 | event_input = torch.nn.functional.one_hot(event_input, num_classes=self.n_class) 32 | event_input = event_input.float() 33 | event_embedding = self.embed(event_input) 34 | rnn_input = torch.cat((event_embedding, time_input.unsqueeze(-1)), dim=-1) 35 | hidden_state, _ = self.rnn(rnn_input) 36 | #print(hidden_state) 37 | event_out = self.event_linear(hidden_state) 38 | time_out = self.time_linear(hidden_state) 39 | return event_out, time_out 40 | 41 | def set_optimizer(self, opt, lr, momentum): 42 | if opt == 'SGD': 43 | return torch.optim.SGD(self.parameters(), lr=lr, momentum=momentum) 44 | else: 45 | return torch.optim.Adam(self.parameters(), lr=lr) 46 | 47 | def train(self, batch, device): 48 | time_tensor, event_tensor = batch 49 | time_tensor.to(device) 50 | event_tensor.to(device) 51 | time_input, time_duration = time_tensor[:, :-1], time_tensor[:, -1] 52 | event_input, event_target = event_tensor[:, :-1], event_tensor[:, -1] 53 | event_out, time_out = self.forward(time_input, event_input) 54 | event_out = event_out[:,-1:].reshape(-1, self.n_class) 55 | loss1 = self.event_criterion(event_out, event_target) 56 | time_out = time_out[:,-1] 57 | loss2 = self.time_criterion(time_out.reshape(-1,1), time_duration.reshape(-1,1)) 58 | loss = loss1 + loss2 59 | loss.backward() 60 | self.optimizer.step() 61 | self.optimizer.zero_grad() 62 | return loss.item(), loss1.item(), loss2.item() 63 | 64 | def predict(self, batch, device): 65 | time_tensor, event_tensor = batch 66 | time_tensor.to(device) 67 | event_tensor.to(device) 68 | time_input, time_duration = time_tensor[:,:-1], time_tensor[:,-1] 69 | event_input, event_target = event_tensor[:,:-1], event_tensor[:,-1] 70 | event_out, time_out = self.forward(time_input, event_input) 71 | time_out = time_out[:,-1] 72 | event_pred = nn.functional.softmax(event_out[:,-1]) 73 | event_pred = torch.max(event_pred, dim=-1)[1].tolist() 74 | return event_pred, time_out 75 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | from argparse import ArgumentParser 2 | import torch 3 | import os 4 | import numpy as np 5 | from scipy import integrate 6 | from sklearn.metrics import mean_squared_error 7 | from math import sqrt 8 | import matplotlib.pyplot as plt 9 | 10 | def data_process(file_name): 11 | f = open(file_name,'r') 12 | time_data = [] 13 | file_data = f.readlines() 14 | f.close() 15 | for line in file_data: 16 | data = line.split(" ") 17 | a_list = [] 18 | for i in range(len(data)): 19 | if data[i] != "\n": 20 | a_list.append(float(data[i])) 21 | time_data.append(a_list) 22 | for i in range(len(time_data)): 23 | line = time_data[i] 24 | time_data[i] = [] 25 | end = 1 26 | while end <= len(line): 27 | time_data[i].append(line[0:end]) 28 | end += 1 29 | time_data = np.array(time_data) 30 | return time_data 31 | 32 | 33 | def generate_type(time_data): 34 | type_data = [] 35 | for line in time_data: 36 | new_line = [] 37 | for item in line: 38 | new_seq = [] 39 | for a_time in item: 40 | new_seq.append(1) 41 | new_line.append(new_seq) 42 | type_data.append(new_line) 43 | return type_data 44 | 45 | 46 | def equation(time_var, time_cif, w, b): 47 | time_guess = time_var*np.exp(time_cif+w*(time_var)+b+ 48 | (np.exp(time_cif+b)-np.exp(time_cif+w*(time_var)+b))/w) 49 | return time_guess 50 | 51 | def intensities(time_var, time_cif, w, b): 52 | ints = np.exp(time_cif+w*(time_var)+b) 53 | return ints 54 | 55 | if __name__ == "__main__": 56 | parser = ArgumentParser() 57 | parser.add_argument("--generate_type", 58 | help="For generated experiment, use true for generate_type", 59 | type=bool, default=True) 60 | parser.add_argument("--test_start", 61 | help="Start of the prediction. For real data, we only predict the last one at -1", 62 | type=int, default=56) 63 | parser.add_argument("--data", type=str, default='Hawkes', 64 | help="Hawkes, self-correcting are choice. Must be the same with the training input") 65 | config = parser.parse_args() 66 | 67 | if torch.cuda.is_available(): 68 | device = torch.device('cuda') 69 | print("You are using GPU acceleration.") 70 | print("Number of CUDAs(cores): ", torch.cuda.device_count()) 71 | else: 72 | device = torch.device("cpu") 73 | print("CUDA is not Available. You are using CPU only.") 74 | print("Number of cores: ", os.cpu_count()) 75 | 76 | data = "data/" + config.data + "/time-test.txt" 77 | 78 | time_test = data_process(data) 79 | if config.generate_type: 80 | type_test = generate_type(time_test) 81 | else: 82 | data_type = "data/"+config.data+"/event-test.txt" 83 | type_test = data_process(data_type) 84 | print("testing file processed.") 85 | 86 | if config.generate_type: 87 | time_test = time_test[0] 88 | type_test = type_test[0] 89 | else: 90 | time_duration = time_duration[:,-1] 91 | type_test = type_test[:,-1] 92 | 93 | model = torch.load("model.pt") 94 | 95 | if config.test_start == -1: 96 | index = 0 97 | else: 98 | index = config.test_start 99 | 100 | 101 | 102 | actual_duration = [] 103 | duration_pred = [] 104 | intensity_pred = [] 105 | event_pred = [] 106 | while index < len(time_test): 107 | time_data = np.diff(time_test[index], axis=-1, prepend=time_test[0]) 108 | actual_duration.append(time_data[-1]) 109 | batch = (torch.tensor([time_data], dtype=torch.float32), torch.tensor([type_test[index]])) 110 | event, time_cif = model.predict(batch, device) 111 | time_cif = time_cif.item() 112 | intensity_w = model.intensity_w.item() 113 | intensity_b = model.intensity_b.item() 114 | func = lambda x: equation(x, time_cif, intensity_w, intensity_b) 115 | duration = integrate.quad(func,0, np.inf)[0] 116 | duration_pred.append(duration) 117 | inten = intensities(duration, time_cif, intensity_w, intensity_b) 118 | intensity_pred.append(inten) 119 | event_pred.append(event[0]) 120 | index += 1 121 | 122 | print("prediction on duration: ", duration_pred) 123 | print("actual duration: ", actual_duration) 124 | print("prediction on types: ", event_pred) 125 | print("intensity: ",intensity_pred) 126 | print("calculating RMSE: ") 127 | rmse = sqrt(mean_squared_error(duration_pred, actual_duration)) 128 | 129 | 130 | f = open("predict-duration.txt", "w") 131 | for t in duration_pred: 132 | f.write(str(t)) 133 | f.write(" ") 134 | f.close() 135 | f = open("predict_type.txt", "w") 136 | for item in event_pred: 137 | f.write(str(item)) 138 | f.write(" ") 139 | f.close() 140 | 141 | 142 | print("generating_time_interval_plot:") 143 | figure, ax = plt.subplots(2,2) 144 | ax[0,0].plot(range(100),actual_duration) 145 | ax[0,0].plot(range(100),duration_pred) 146 | ax[0,1].plot(range(100),intensity_pred) 147 | ax[1,0].bar(x=1, height=rmse) 148 | ax[1,0].annotate(str(round(rmse,3)),xy=[1, rmse]) 149 | ax[1,1].set_visible(False) 150 | figure.tight_layout() 151 | plt.savefig("result.png") 152 | 153 | 154 | 155 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | from argparse import ArgumentParser 2 | import torch 3 | import os 4 | import model 5 | import random 6 | import numpy as np 7 | 8 | 9 | 10 | 11 | def data_process(file_name, seq_len): 12 | f = open(file_name,'r') 13 | time_data = [] 14 | file_data = f.readlines() 15 | f.close() 16 | for line in file_data: 17 | data = line.split(" ") 18 | a_list = [] 19 | for i in range(len(data)): 20 | if data[i] != "\n": 21 | a_list.append(float(data[i])) 22 | time_data.append(a_list) 23 | for i in range(len(time_data)): 24 | line = time_data[i] 25 | time_data[i] = [] 26 | end = seq_len 27 | while end <= len(line): 28 | start = end-seq_len 29 | time_data[i].append(line[start:end]) 30 | end += 1 31 | time_data = np.array(time_data) 32 | time_duration = np.diff(time_data, axis=-1, prepend=time_data[:,:,:1]) 33 | return time_data, time_duration 34 | 35 | 36 | def generate_type(time_data): 37 | type_data = [] 38 | for line in time_data: 39 | new_line = [] 40 | for item in line: 41 | new_seq = [] 42 | for a_time in item: 43 | new_seq.append(1) 44 | new_line.append(new_seq) 45 | type_data.append(new_line) 46 | return type_data 47 | 48 | 49 | if __name__ == "__main__": 50 | """The code below is used to set up command line inputs. """ 51 | parser = ArgumentParser() 52 | parser.add_argument("--n_class", 53 | help="Number of types in the dataset, default is 2", type=int, default=2) 54 | parser.add_argument("--hid_dim", type=int, default=1) 55 | parser.add_argument("--n_layers", type=int, default=1) 56 | parser.add_argument("--lr", type=float, default=0.01) 57 | parser.add_argument("--momentum", type=float, default=0.01) 58 | parser.add_argument("--epochs", type=int, default=30) 59 | parser.add_argument("--opt", type=str, default='Adam') 60 | parser.add_argument("--generate_type", 61 | help="For generated experiment, use true for generate_type", 62 | type=bool, default=True) 63 | parser.add_argument("--read_model", type=bool, default=False) 64 | parser.add_argument("--seq_len", type=int, default=10) 65 | parser.add_argument("--data", type=str, default='Hawkes', 66 | help="Hawkes, self-correcting are choices") 67 | config = parser.parse_args() 68 | 69 | 70 | """The code below is to get the training data""" 71 | file_name = 'data/' + config.data + "/time-train.txt" 72 | time_train, time_duration = data_process(file_name, config.seq_len) 73 | if config.generate_type: 74 | type_data = generate_type(time_train) 75 | else: 76 | data_type = "data/"+config.data+"/event_test.txt" 77 | type_data = data_process(data_type) 78 | print("training file processed.") 79 | 80 | 81 | """The code below is used to set up customized training device on computer""" 82 | if torch.cuda.is_available(): 83 | device = torch.device('cuda') 84 | print("You are using GPU acceleration.") 85 | print("Number of CUDAs(cores): ", torch.cuda.device_count()) 86 | else: 87 | device = torch.device("cpu") 88 | print("CUDA is not Available. You are using CPU only.") 89 | print("Number of cores: ", os.cpu_count()) 90 | 91 | 92 | """decide whether to used pred-trained model to train again""" 93 | if config.read_model: 94 | model = torch.load("model.pt") 95 | else: 96 | model = model.RMTPP(config, device) 97 | for parameter in model.parameters(): 98 | parameter.data.fill_(random.uniform(0.4, 0.5)) 99 | 100 | 101 | """Trianing process""" 102 | for epc in range(config.epochs): 103 | c = list(zip(time_train, type_data)) 104 | random.shuffle(c) 105 | time_train, type_data = zip(*c) 106 | loss_total = 0 107 | loss_type = 0 108 | loss_time = 0 109 | for index in range(len(time_train)): 110 | batch = (torch.tensor(time_duration[index], dtype=torch.float32), torch.tensor(type_data[index])) 111 | loss, loss1, loss2 = model.train(batch, device) 112 | loss_total += loss 113 | loss_type += loss1 114 | loss_time += loss2 115 | print("In epochs {0}, total loss: {1}, type loss: {2}, time loss: {3}".format( 116 | epc, loss_total/len(time_train), loss_type/len(time_train), loss_time/len(time_train) 117 | )) 118 | print("saving model") 119 | torch.save(model, "model.pt") 120 | print("training done!") --------------------------------------------------------------------------------