Repository: benedekrozemberczki/diff2vec
Branch: master
Commit: 12d915539e43
Files: 10
Total size: 2.0 MB

Directory structure:
gitextract_e04igrgu/

├── .github/
│   └── FUNDING.yml
├── LICENSE
├── README.md
├── data/
│   ├── politician_edges.csv
│   └── restaurant_edges.csv
├── output/
│   └── restaurant.csv
└── src/
    ├── diffusion_2_vec.py
    ├── diffusiontrees.py
    ├── helper.py
    └── subgraphcomponents.py

================================================
FILE CONTENTS
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FILE: .github/FUNDING.yml
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github: [benedekrozemberczki]


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FILE: LICENSE
================================================
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Also add information on how to contact you by electronic and paper mail.

  If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:

    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License.  Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".

  You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.

  The GNU General Public License does not permit incorporating your program
into proprietary programs.  If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library.  If this is what you want to do, use the GNU Lesser General
Public License instead of this License.  But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.


================================================
FILE: README.md
================================================
Diff2Vec
===============================

[![Arxiv](https://img.shields.io/badge/ArXiv-2001.07463-orange.svg)](https://arxiv.org/pdf/2001.07463.pdf) [![codebeat badge](https://codebeat.co/badges/6050dda6-4bd9-4977-985a-67cd4686d410)](https://codebeat.co/projects/github-com-benedekrozemberczki-diff2vec-master)
[![repo size](https://img.shields.io/github/repo-size/benedekrozemberczki/diff2vec.svg)](https://github.com/benedekrozemberczki/diff2vec/archive/master.zip) [![benedekrozemberczki](https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=benrozemberczki) 
<p align="justify">
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods. 
</p>

<p align="center">
  <img width="500" src="diff2vec.jpeg">
</p>

The model is now also available in the package [Karate Club](https://github.com/benedekrozemberczki/karateclub).


This repository provides a reference implementation for **Diff2Vec** as described in the paper:

> [Fast Sequence Based Embedding with Diffusion Graphs](https://arxiv.org/abs/2001.07463)
> [Benedek Rozemberczki](http://homepages.inf.ed.ac.uk/s1668259/) and  [Rik Sarkar](https://homepages.inf.ed.ac.uk/rsarkar/).
> International Conference on Complex Networks, 2018.

----------------------------

### Citing

If you find Diff2Vec useful in your research, please consider citing the following paper:
```bibtex
>@inproceedings{rozemberczki2018fastsequence,  
  title={{Fast Sequence Based Embedding with Diffusion Graphs}},  
  author={Rozemberczki, Benedek and Sarkar, Rik},  
  booktitle={International Conference on Complex Networks},  
  year={2018},  
  pages={99--107}
 }
```
### Requirements

The codebase is implemented in Python 3.5.2 | Anaconda 4.2.0 (64-bit).

```
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
gensim            3.6.0
networkx          2.4
joblib            0.13.0
logging           0.4.9.6  
```

### Datasets
<p align="justify">
The code takes an input graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. A sample graph for the `Facebook Restaurants` dataset is included in the  `data/` directory.</p>

### Options

<p align="justify">
Learning of the embedding is handled by the `src/diffusion_2_vec.py` script which provides the following command line arguments.</p>

#### Input and output options
```
  --input    STR     Path to the edge list csv.            Default is `data/restaurant_edges.csv`
  --output   STR     Path to the embedding features.       Default is `emb/restaurant.csv`
```

#### Model options
```
  --model                    STR      Embedding procedure.                      Default is `non-pooled`
  --dimensions               INT      Number of embedding dimensions.           Default is 128.
  --vertex-set-cardinality   INT      Number of nodes per diffusion tree.       Default is 80.
  --num-diffusions           INT      Number of diffusions per source node.     Default is 10.
  --window-size              INT      Context size for optimization.            Default is 10.
  --iter                     INT      Number of ASGD iterations.                Default is 1.
  --workers                  INT      Number of cores.                          Default is 4.
  --alpha                    FLOAT    Initial learning rate.                    Default is 0.025.
```

### Examples

<p align="justify">
The following commands learns a graph embedding and writes it to disk. The first column in the embedding file is the node ID.
</p>
Creating an embedding of the default dataset with the default hyperparameter settings.

```
python src/diffusion_2_vec.py
```
Creating an embedding of an other dataset the `Facebook Politicians`.

```
python src/diffusion_2_vec.py --input data/politician_edges.csv --output output/politician.csv
```
<p align="justify">
Creating an embedding of the default dataset in 32 dimensions, 5 sequences per source node with maximal vertex set cardinality of 40.</p>

```
python src/diffusion_2_vec.py --dimensions 32 --num-diffusions 5 --vertex-set-cardinality 40
```
-----------------------------------------------------------------

**License**

- [GNU](https://github.com/benedekrozemberczki/diff2vec/blob/master/LICENSE)

-----------------------------------------------------------------


================================================
FILE: data/politician_edges.csv
================================================
node_1,node_2
0,1972
0,5111
0,138
0,3053
0,1473
0,4978
0,4523
0,2334
0,1038
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5360,5699
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904,4151
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907,1443
907,4861
907,5482
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235,2969
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1007,5083
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1021,3173
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1024,1266
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357,4218
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1027,3411
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378,2580
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1031,5416
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1038,1972
1038,3372
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1038,4674
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1038,4978
1038,3883
1038,4020
1038,4523
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1038,4630
1039,4551
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1042,4928
1042,3513
1042,2259
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1051,2363
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647,1357
647,2397
647,3739
647,1428
1069,4877
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1079,4562
1079,5542
1079,1144
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1080,2347
1080,2160
1080,5584
1080,5543
1080,3752
1080,1340
1080,2060
1080,3595
1080,5305
1080,1429
1080,5538
1080,1456
1080,2815
1080,5560
1080,4835
1080,4071
1080,4918
1080,1648
1080,4920
1081,2851
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3403,2202
3403,4439
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3403,5545
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3403,3611
3403,4874
3403,2425
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3403,3671
1087,2111
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1103,2760
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1129,4413
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1651,3363
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1248,2166
1248,3541
1248,4409
1248,4225
1249,5305
1249,2815
1249,4835
1249,4918
1249,5119
1249,3994
1249,5584
1249,4158
1249,2060
1249,1429
1249,1456
1249,3595
1249,2160
1249,1263
1249,2546
1249,3752
1249,5560
1249,5538
1249,4071
1249,2347
1249,5543
1249,1340
1249,1409
1250,5705
1250,1983
1250,3229
1250,3305
1250,4789
1250,3315
1250,1397
1250,4333
1250,2035
1250,2736
1250,2379
1250,530
1250,5355
1250,4193
1251,2777
1251,3792
1251,3719
1251,1323
1252,1446
1252,4954
1252,4182
1252,4210
1252,5558
1252,3504
1252,2292
1252,1885
1252,3748
1252,4637
1252,5679
1252,1980
1252,3704
1252,1267
1252,4122
1252,5391
1252,4385
1252,4175
1252,3203
1252,3828
1253,2229
1254,3541
1254,1707
1254,3079
1254,5867
1255,2576
1255,3840
1255,5756
1867,2674
1867,5884
1867,3619
1867,5192
1867,1337
1867,3453
1867,4726
1867,5198
1867,3718
1867,4851
1867,2857
1257,1291
1257,2304
1257,4014
1257,4921
1257,5800
1257,3946
1257,3897
1877,4976
1877,5028
1259,4416
1259,2145
1259,867
1259,4904
1259,1646
1259,4233
1259,2698
1260,1815
1260,3520
1260,4140
1260,2557
1260,1933
1260,4477
1261,2615
1261,4936
1261,4318
1262,2011
1262,2583
1262,4746
1263,5285
1263,4542
1263,4414
1263,5444
1263,541
1263,2330
1263,4835
1263,5305
1263,2217
1263,2060
1263,3169
1263,3523
1263,1372
1263,4036
1263,5119
1263,4920
1263,3994
1263,4918
1263,3752
1263,4158
1263,3530
1263,4159
1263,2820
1263,1429
1263,2131
1263,2417
1263,1456
1263,4395
1263,3325
1263,3595
1263,4617
1263,1409
1263,2797
1263,5625
1263,2389
1263,5584
1263,1386
1263,5560
1263,5538
1263,2920
1263,5481
1263,2394
1263,4296
1263,1439
1263,4071
1263,1648
1263,4143
1263,2013
1263,4766
1263,2347
1263,2160
1263,5572
1263,3297
1263,5543
1263,5615
1263,4601
1263,5900
1263,4627
1263,4872
1263,1340
1263,1626
1263,3308
1263,3498
1263,5692
1263,5300
1263,5396
1263,3239
1263,2928
1264,2266
1264,2811
1264,3765
1265,3852
1265,1357
1265,1952
1266,4980
1266,3755
1266,4240
1266,1556
1266,4258
1267,966
1267,2292
1267,4637
1267,4182
1267,5558
1267,3504
1921,4274
1921,1946
1921,5747
1921,4340
1921,3354
1921,2128
1921,5882
1921,2357
1921,1864
1921,1478
1921,2084
1921,3019
1921,5743
1921,5228
1921,5772
1921,5052
1921,4422
1921,4444
1921,3597
1921,5029
1921,2689
1921,3576
1921,5681
1921,4328
1921,5870
1921,1986
1921,4572
1921,1443
1921,5439
1921,2598
1921,2831
1921,1606
1921,5643
1921,2602
1921,4874
1921,1472
1921,3519
1269,755
1269,2327
1269,1485
1269,3479
1269,2727
1269,2654
1269,5249
1269,3014
1269,4829
1269,4836
1270,5048
1270,4974
1270,5418
1270,4234
1270,1348
1270,1952
1270,3744
1270,1568
1270,3128
1270,5382
1270,3852
1270,4665
1270,2003
1270,3197
1270,4162
1270,5511
1270,2893
1270,4243
1270,2523
1270,3557
1270,5237
1270,3234
1270,2099
1270,4970
1270,1660
1271,1400
1271,5176
1271,2791
1271,4180
1271,1371
1271,3219
1271,3076
1271,4878
1271,3110
1271,1805
1271,1595
1271,2059
1271,2770
1271,3335
1271,3336
1271,4160
1271,2515
1271,3363
1271,4406
1271,5726
1271,4964
1271,5797
1271,1853
1271,1414
1271,1773
1271,2041
1271,5416
1271,5871
1271,4602
1271,3649
1271,2298
1271,4972
1271,3587
1271,5072
4180,4081
4180,1400
4180,4032
4180,3281
4180,4418
4180,1474
4180,3782
4180,3829
4180,5808
4180,1371
4180,3459
4180,3219
4180,2906
4180,4160
4180,1805
4180,4838
4180,1595
4180,2059
4180,1766
4180,1686
4180,5076
4180,2770
4180,2572
4180,1641
4180,4878
4180,4284
4180,2515
4180,3363
4180,2819
4180,2883
4180,1711
4180,4901
4180,3335
4180,5726
4180,5042
4180,5137
4180,5414
4180,2562
4180,1709
4180,4622
4180,2068
4180,3094
4180,1414
4180,2346
4180,2041
4180,3324
4180,5416
4180,5441
4180,4625
4180,1773
4180,3539
4180,4602
4180,5871
4180,3801
4180,4476
4180,4090
4180,5691
4180,1470
4180,5797
4180,4972
4180,3709
4180,5072
1273,1357
1273,1428
1273,5325
1273,3739
1273,4516
1273,3148
1273,2419
1273,1864
1273,2937
1274,4517
1274,1434
1274,5901
1274,3867
1274,5299
1274,5486
1274,3903
1274,4778
1275,1367
1275,5861
1275,5564
1275,5014
1275,1374
1275,1625
1275,3399
1275,4176
1275,5426
1275,4398
1275,2573
1276,2066
1276,2371
1276,2904
1276,2313
1276,2125
1276,4153
1276,3977
1276,3379
1276,2993
1276,4096
1972,5111
1972,3665
1972,3377
1972,3053
1972,4033
1972,2524
1972,253
1972,4978
1972,4523
1972,2334
1972,4185
1972,3874
1972,2676
1972,2998
1972,3622
1972,5292
1972,3489
1972,5455
1972,2755
1972,1578
1972,2569
1972,2639
1972,3842
1972,1473
1972,5866
1972,3883
1972,5154
1972,2759
1972,5800
1972,3372
1972,5566
1972,4157
1972,4674
1972,2900
1972,2852
1972,4537
1972,1547
1972,3099
1972,4924
1972,5855
1972,4253
1972,2051
1972,4630
1278,3680
1278,2607
1278,3445
1278,1864
1278,5066
1279,3665
1279,1632
1279,1344
1279,5779
1279,4761
1279,3170
1279,5093
1279,3242
1279,5885
1279,1796
1279,1675
1279,3489
1279,4749
1279,1849
1279,4185
1279,2713
1279,2759
1279,1831
1279,4598
1279,2015
1279,4047
1279,2524
1279,1909
1279,3099
1279,2527
1279,2647
1279,5785
1279,3374
1279,5855
1279,4253
1279,4911
1279,5599
1280,2965
1280,3152
1281,2319
5524,4081
5524,4032
5524,4707
5524,5489
5524,5880
5524,3829
5524,2791
5524,5280
5524,2026
5524,2153
5524,4023
5524,4266
5524,4570
5524,5180
5524,3569
5524,2770
5524,4113
5524,4080
5524,1800
5524,3243
5524,2843
5524,3999
5524,4427
5524,3409
5524,1846
5524,2105
5524,5148
5524,4643
5524,2485
5524,3879
5524,5562
5524,4211
5524,5778
5524,1489
5524,5826
5524,5415
5524,2882
5524,2232
5524,1723
5524,5434
5524,2393
5524,4406
5524,2453
5524,5753
5524,2059
5524,4602
5524,2872
5524,5326
5524,4517
5524,2645
5524,5151
5524,2894
5524,1678
5524,4595
5524,3417
5524,4386
5524,4831
5280,5148
5280,2485
5280,1723
5280,2153
5280,4494
1282,1931
1282,3162
1282,2979
1282,2527
1282,4804
1282,5763
1282,1996
1282,4253
1282,3242
1283,4811
1284,2190
1284,3208
1284,1306
1284,1779
1284,3421
1284,4904
1284,1790
1284,2145
1284,2698
1284,4391
2017,2075
2017,3719
2017,2739
2017,1812
2017,2536
2017,2878
2017,1323
2017,5110
2017,1997
2017,3173
2017,4745
2017,2293
2017,1845
2017,3721
2017,5453
2017,4247
2017,3693
2017,5344
2017,5297
2017,3067
2017,5033
2017,1806
2017,4303
2017,5036
2017,5061
2017,5485
2017,4227
1286,5120
1287,2238
1287,3008
1287,2705
1288,5713
1288,4340
1288,5333
1288,3354
1288,5072
1288,5882
1288,1864
1288,4459
1288,3617
1288,5125
1288,3576
1288,5480
1288,4874
1288,5191
1288,1299
1288,5419
1288,5643
1288,2646
1288,3925
1288,2855
1288,5041
1289,5163
1289,5416
1289,1453
1289,1595
1289,5816
1289,4166
1290,4733
1290,2077
1290,4707
1290,2474
1290,4651
1290,3101
1290,5865
1290,1469
1290,2592
1290,5327
1290,5328
1291,1291
1291,1463
1291,4975
1291,3703
1291,4879
1291,1488
1291,2062
1291,3396
1291,3663
1291,2056
1292,3558
1292,3537
1292,769
1292,2454
1292,5742
1292,2061
1292,4654
1292,5716
1292,3482
1292,4508
1292,2306
1292,4059
1292,2778
1292,2495
1292,5139
1292,3610
1293,282
1293,4084
1293,1662
1293,2789
1293,1601
1293,4923
1293,5740
1293,5039
1293,2130
1293,3604
1294,2780
1294,3387
1294,3181
1294,3576
1294,4792
1294,5496
1294,3887
1294,2429
1294,3936
1294,5800
1294,3398
1294,2700
1294,5118
1294,4652
1294,2950
1294,1928
1295,3008
1296,1956
4187,1882
4187,1954
4187,5864
1299,4874
1299,1864
1300,3731
1300,2650
1300,3246
1300,2344
1301,5066
1301,1864
1301,2042
1301,4555
1302,5666
1302,1912
1302,4795
1303,2760
1303,709
1303,1333
1303,5644
1303,1950
1303,3833
1304,2338
1304,2111
1304,2363
1304,1707
1304,5494
1304,3567
1304,5867
1304,1725
1304,5800
1304,5633
1304,2386
1304,2546
1305,184
1305,4491
1305,4885
1305,4360
1305,2934
1306,4670
1306,4818
1306,2088
1306,1329
1306,5286
1306,4391
1306,5337
1306,1779
1306,2145
1306,134
1306,4690
1306,5024
1307,4979
1307,4374
1308,4299
1308,3623
1308,3008
1308,3068
1309,2886
1309,5634
1309,2760
1310,4656
1310,4236
1310,4874
1310,2189
1311,4453
1311,3575
1311,2360
1312,1916
1313,4837
1313,2919
1313,3670
1314,1633
1314,4705
1315,1954
1316,1954
1317,3008
1318,2646
1318,1581
1318,5643
1318,4459
1318,3617
1319,2616
4021,3288
4021,2218
4021,1374
4021,1625
4021,5665
4021,2624
4021,3873
1321,5101
1323,1366
1323,5650
1323,5713
1323,5553
1323,1474
1323,4440
1323,2860
1323,2997
1323,2743
1323,4768
1323,4153
1323,1864
1323,5157
1323,1704
1323,1668
1323,3462
1323,2885
1323,3719
1323,4585
1323,2940
1323,3672
1323,3680
1323,4422
1323,3228
1323,3387
1323,2777
1323,5118
1323,5800
1323,2277
1323,3604
1323,4729
1323,2137
1323,5066
1323,4829
1323,4107
1323,3792
225,5416
225,2819
225,1709
225,1595
1326,3008
1327,4318
1327,2049
1327,2615
1327,3326
1327,5732
1327,1586
1327,4342
1327,4395
1327,2108
1328,4413
1328,4796
1328,5471
1328,3317
1328,2448
1328,3699
1328,2948
1328,2565
1328,1618
1328,5729
1329,2190
1329,2908
1329,867
1329,3208
1329,2088
1329,2048
1329,1779
1329,5337
1329,4070
1329,1790
1329,4391
1329,2145
1329,2698
1329,4214
1330,3685
1330,4297
1331,3857
1331,3484
1331,1562
1331,3800
1331,4728
1331,2107
1331,5468
1332,1908
1332,3841
1332,867
1332,2698
2339,4323
2339,5430
2339,3211
2339,5554
2339,5479
2339,2948
2339,2736
2339,2930
2339,5387
2339,5420
2339,3367
2339,2612
2339,4183
2339,3696
1334,3471
1334,2633
1334,1479
1334,2825
1335,4716
1335,2698
1336,4714
1336,4898
1336,3443
1336,3030
1337,2674
1337,5884
1337,3619
1337,5192
1337,5198
1337,3718
1337,3064
1338,4599
1339,2763
1339,1973
1339,1588
1339,4938
1339,5841
1339,5745
1339,1230
1339,4772
1339,2966
1339,4115
1339,2969
1339,5167
1339,5663
1339,5235
1339,4846
1339,1855
1339,5374
1339,5206
1340,5444
1340,3258
1340,5625
1340,5264
1340,2217
1340,4835
1340,4920
1340,3994
1340,2413
1340,1353
1340,1454
1340,2417
1340,1456
1340,4190
1340,3000
1340,5538
1340,3630
1340,5543
1340,5734
1340,2330
1340,3523
1340,3830
1340,2331
1340,2815
1340,1372
1340,5119
1340,3548
1340,4918
1340,2060
1340,2914
1340,3023
1340,5560
1340,2160
1340,2546
1340,3752
1340,3029
1340,5300
1340,3297
1340,4872
1340,2928
1340,4414
1340,5572
1340,2651
1340,5305
1340,4505
1340,1791
1340,1597
1340,1797
1340,1409
1340,5584
1340,2757
1340,3530
1340,5417
1340,1441
1340,5692
1340,3239
1340,541
1340,2197
1340,4523
1340,4158
1340,4159
1340,1429
1340,3595
1340,4617
1340,845
1340,4821
1340,5615
1340,4071
1340,5900
1340,4354
1340,2402
1340,3983
1341,4643
1341,5489
1341,2485
1341,5880
1341,4113
1341,2393
1341,2153
1341,2894
1341,5882
1341,5434
1341,4080
1341,1678
1342,5066
1342,2406
1342,3719
1342,3781
1342,5227
1342,1505
1342,1864
1342,5279
1343,1842
1343,3987
1343,5066
1343,4408
1343,1923
1343,2953
1343,3166
1343,3975
1344,3665
1344,1632
1344,2702
1344,2086
1344,5574
1344,1719
1344,1996
1344,3170
1344,5093
1344,3762
1344,3242
1344,4438
1344,4185
1344,1831
1344,1675
1344,4020
1344,3489
1344,4761
1344,4749
1344,1849
1344,2230
1344,1621
1344,3140
1344,2713
1344,3684
1344,2759
1344,3162
1344,4598
1344,2015
1344,4047
1344,5566
1344,2524
1344,3099
1344,1466
1344,3982
1344,2979
1344,3817
1344,2647
1344,5785
1344,3374
1344,5855
1344,4253
1344,4498
1344,4911
1344,5885
1345,3689
1345,3432
1345,2856
1345,5329
1346,3947
1346,5797
1348,3234
1348,2134
1348,1988
1348,4974
1348,3557
1348,2893
1348,4243
1348,4970
1348,4665
1348,2729
1348,1952
1348,3744
1348,5752
1349,3660
1349,2633
1349,5074
1349,5082
1350,3058
1350,4689
1350,3124
1350,4974
1350,5128
1351,2303
1351,5644
1352,3402
1353,1409
1353,5584
1353,5543
1353,3752
1353,4158
1353,2060
1353,1454
1353,3595
1353,5305
1353,1429
1353,5538
1353,1456
1353,2815
1353,5560
1353,4835
1353,4071
1353,4918
1353,4920
1355,3326
1355,3389
1356,755
1356,5135
1356,1784
1356,5325
1356,3676
1356,2960
1356,3786
1356,5161
1356,5162
1356,3287
1356,1865
1356,3086
1356,1706
1356,2335
1356,5456
1356,5457
1356,3927
1356,1388
1356,3844
1356,5215
1356,1361
1356,2473
1356,1623
1356,1298
1356,2681
1356,4657
1356,4200
1356,2018
1356,2456
1356,3395
1356,2495
1356,4994
1356,3374
1356,2957
1357,4440
1357,3990
1357,3148
1357,3337
1357,1819
1357,1640
1357,2419
1357,5823
1357,1428
1357,5894
1357,2892
1357,3819
1357,2397
1357,3659
1357,3827
1357,4419
1357,1685
1358,1703
1358,1916
1358,3807
1358,3428
1358,4667
1358,2161
1359,3227
1359,4801
1360,5343
1360,2187
1360,1390
1360,3267
1360,1541
1360,1964
1361,1388
1362,5416
1363,2969
1363,1855
1363,1588
1363,1973
1363,2815
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2905,3503
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276,3576
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1630,5018
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5212,4032
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5212,4525
5212,2723
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5212,2248
5212,4857
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2291,3008
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1906,5281
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1916,3091
1916,5081
1916,2731
1916,4695
1916,3031
1916,2259
1916,5677
1916,3438
1916,5905
1916,3476
1916,3734
1916,4579
1917,5580
1917,3231
1918,4694
1918,3541
1918,3079
1918,2340
1918,5867
1918,2314
1918,2386
1918,3717
1918,2412
1918,5592
1918,5879
1919,3833
1919,5644
1919,3668
1919,1943
1919,4030
1919,5291
4647,3510
4647,5353
4647,3008
1268,4081
1268,1434
1268,5505
1268,4763
1268,3999
1268,4032
1268,4160
1922,5101
5750,3441
5750,3306
5750,4257
5750,4341
5750,3219
5750,3334
5750,4585
5750,2685
5750,5891
5750,2515
5750,5277
5750,1990
5750,4470
5750,2041
5750,5441
5750,4602
5750,2551
5750,3543
5750,2648
5750,2738
5750,4972
5750,5520
1924,5670
1924,3008
1924,2440
1924,5120
1924,5708
1924,3638
1924,5840
1924,5350
1924,3870
1924,3236
1925,4656
1925,2189
1925,4874
1925,4236
1926,5307
1019,1247
1927,3490
1927,3980
1927,4959
1927,1114
1928,4031
1928,3387
1928,2695
1928,3074
1928,5142
1928,5306
1928,5198
1928,2549
1928,2188
1928,3011
1928,2698
1928,2937
1928,5676
1929,3833
1929,2840
1929,5853
1929,5491
1930,5178
1930,3547
1930,2408
1930,2879
1930,3617
1930,3972
1930,4692
1930,4039
1930,5125
1930,3576
1930,5346
1930,3628
1930,4700
1930,5899
1930,2009
1930,2598
1930,2725
1930,5643
1930,5780
1930,4248
1930,2325
1930,4150
1930,5041
1930,2052
1931,2702
1931,2086
1931,5446
1931,4810
1931,5574
1931,5763
1931,2171
1931,1976
1931,2890
1931,3817
1931,5779
1931,3369
1931,2540
1931,3162
1931,5566
1931,2558
1931,4293
1931,2979
1931,2527
1931,4804
1931,5065
1931,4253
1932,3759
1932,4459
1932,5178
1932,3147
1932,4234
1932,5882
1932,2518
1932,3285
1932,3460
1932,2725
1932,3020
1932,5052
1932,5713
1932,4371
1932,4740
1932,3597
1932,3576
1932,3491
1932,3230
1932,4375
1932,5289
1932,3617
1932,3049
1932,3207
1932,2122
1932,2951
1932,5643
1932,4248
1932,2646
1932,2831
1932,4849
1932,5393
1932,4874
1933,3520
1933,2327
1933,3963
1933,2557
1933,3014
1933,3311
1933,3312
1933,5099
1933,2511
1933,3770
1933,4426
1933,5127
1933,5800
1933,2038
1933,2011
1933,4009
1933,2760
1933,5517
1933,4477
1933,4829
1934,4874
1935,4913
1935,2449
1935,5863
1936,5166
1936,5536
1936,3986
1936,4735
1936,5082
1936,4594
1936,4219
1936,5068
1936,5243
1936,3743
1936,5366
1937,5412
1938,1971
1938,4789
1938,2937
1938,2379
1938,3621
1938,3679
1938,4193
1938,2298
1938,3315
1938,5718
1938,2141
1938,530
1938,5321
1938,5705
1938,3182
1938,4883
1938,3637
1938,5355
1938,4333
1938,4202
1938,5684
1938,4759
1939,4979
1939,4318
1939,2615
1939,4219
1939,2049
1939,4395
1939,3389
1939,5526
1939,3157
1940,2679
1940,3345
1940,2945
1940,4613
1941,4927
1941,3270
1941,3799
1942,3408
1942,2695
1943,5861
1943,3668
1943,709
1943,4466
1943,4545
1943,3833
1943,1333
1943,2638
1943,2366
1943,3341
1943,5828
1943,4458
1943,2760
1943,5644
1943,4909
1943,4249
1944,966
1944,4448
1944,5558
1944,3504
1945,2167
1945,3959
1945,5413
1946,3574
1946,3617
1946,5184
1946,4874
1946,2357
1946,4160
1947,1230
1948,2591
1948,2149
1948,4874
1948,2863
1948,2317
1949,2044
1949,4921
1949,3713
1949,4014
1949,3405
1949,2364
1949,4058
1949,3669
1949,1991
1949,4697
1949,4693
1950,1333
1950,709
1950,2760
1951,2722
1951,2025
1951,4451
1951,3726
1951,2364
1951,4693
1951,3669
1951,2685
1951,1991
1951,4697
1951,5906
1951,5410
1951,5532
1952,5273
1952,4974
1952,5418
1952,4234
1952,2862
1952,3460
1952,3744
1952,3058
1952,3128
1952,5382
1952,3557
1952,5048
1952,3224
1952,2941
1952,3852
1952,2630
1952,4665
1952,2003
1952,2523
1952,4919
1952,4371
1952,2134
1952,4162
1952,4643
1952,5511
1952,2893
1952,4243
1952,2492
1952,3197
1952,4198
1952,3025
1952,5752
1952,5237
1952,3234
1952,1988
1952,4702
1952,4358
1952,2900
1952,5109
1952,4459
1952,5497
1952,2099
1952,4970
1952,2717
1952,2050
1952,2983
1953,3173
1953,2812
1953,5344
1953,3541
1953,3661
1953,2684
1953,2296
1953,1997
1953,4247
1954,4281
1954,5872
1954,2457
1954,3784
1954,5193
1954,4947
1954,3738
1954,5864
1955,1213
1955,3509
1955,5841
1955,5559
1955,5206
1955,5745
1956,4714
1956,5654
1956,3443
1957,2181
1957,2374
1957,3580
1958,5295
1958,4255
1958,4518
1958,4547
1958,3899
1958,4364
1958,3127
1958,5800
1958,5423
1959,3726
1960,2214
1960,4432
1960,2696
1960,4482
1961,4177
1961,755
1961,2327
1961,2353
1961,3353
1961,4952
1961,3479
1961,3189
1961,3014
1961,4836
1961,5739
1961,2455
1961,3763
1961,4582
1961,4484
1961,5573
1961,4585
1961,3950
1961,5249
1961,3558
1961,2184
1961,769
1961,4508
1961,2094
1961,5127
1961,4048
1961,2495
1961,3537
1961,2641
1961,3934
1961,4074
1961,2045
1961,3033
1961,3474
1961,3035
1961,5519
1961,4829
1961,3562
1962,2105
1962,4643
1962,5489
1962,5562
1962,4113
1962,4023
1962,3777
1962,2894
1962,5434
1962,5891
1962,3952
1963,3415
1964,5253
1964,4431
1964,1016
1964,3257
1964,3583
1965,5357
1965,2948
1965,2880
1965,4183
1965,3305
1965,2513
1965,2612
1965,4467
1965,2034
1965,3531
1965,4005
1965,3367
1965,3094
1965,4754
1965,5086
1965,5729
1965,3099
1965,5730
1965,2733
1965,4759
1965,4205
1965,3009
1965,5737
1965,5554
1965,2817
1965,2118
1965,4395
1965,4217
1965,2634
1965,2639
1965,5387
1965,5388
1965,3117
1965,5479
1965,3211
1965,4496
1965,2736
1965,4413
1965,4500
1965,4789
1965,3849
1965,2868
1965,2565
1965,2942
1965,3226
1965,4323
1965,1995
1965,3229
1965,3315
1965,3576
1965,3957
1965,4883
1965,5321
1965,4967
1965,5055
1965,4519
1965,3866
1965,5420
1965,3329
1965,541
1965,5557
1965,3955
1965,5163
1965,3696
1965,4796
1965,3506
1965,3699
1965,1003
1965,2072
1965,4815
1965,4721
1965,4900
1965,4175
1965,5168
1965,5800
1965,3603
1965,4156
1965,3321
1965,5705
1965,5355
1965,4079
1965,3609
1966,3604
1967,3712
1967,2442
1967,5626
1968,5416
1968,2500
1968,5873
1968,3487
1968,2401
1968,3793
1968,4041
1969,3988
1969,2374
1969,4620
1969,3151
1970,1421
1970,2059
1970,3627
1970,3335
1971,4789
1971,5718
1971,2379
1971,2141
1971,4193
1971,3229
1971,2634
1971,530
1971,878
1971,5705
1971,3182
1971,3211
1971,5355
1971,4333
1971,4202
1971,2925
1971,2736
1277,5206
1277,4772
1277,4115
1277,1230
1973,2969
1973,5206
1973,5167
1973,5663
1973,2966
1973,777
1973,4772
1973,5841
1973,1230
1974,3090
1974,3631
1976,2171
1976,2086
1976,4914
1976,5574
1976,2558
1976,2890
1976,3817
1976,3369
1976,3162
1976,4598
1976,5566
1976,4293
1976,3982
1976,2979
1976,4804
1976,5621
1976,5855
1976,4253
1976,5599
1977,2013
1977,2347
1977,2060
1977,5625
1977,5305
1977,5538
1977,4872
1977,4395
1977,4296
1977,2160
1978,2798
1978,4739
1978,3027
1978,4144
1978,2842
1978,5474
1978,4100
1978,2175
1978,2469
1978,4487
1979,5900
1980,3238
1980,3878
1980,2741
1980,5391
1980,3264
1980,4565
1980,2292
1980,3130
1980,4954
1980,1430
1980,3413
1980,3203
1980,5591
1980,4108
1980,5679
1981,5058
1981,4543
1982,5705
1982,3229
1982,955
1982,5355
1982,4333
1982,1394
1982,878
1983,4789
1983,4541
1983,5718
1983,3995
1983,2379
1983,2141
1983,2736
1983,3768
1983,2612
1983,4193
1983,2034
1983,3229
1983,2634
1983,5479
1983,5684
1983,878
1983,5055
1983,3117
1983,5705
1983,2035
1983,3182
1983,4883
1983,5355
1983,4333
1983,4202
1983,2925
1983,530
1983,4228
1983,4759
1984,4081
1984,4032
1984,3281
1984,2988
1984,2173
1984,5069
1984,2537
1984,4878
1984,4618
1984,2690
1984,5638
1984,5726
1984,5868
1984,5416
1984,5871
1984,4602
1984,4972
1985,2615
1985,4395
1985,5093
1986,4274
1986,5419
1986,4700
1986,2918
1986,4340
1986,3354
1986,2831
1986,4874
1986,4917
1986,2195
1986,2357
1986,3519
1986,4328
1987,2898
1987,2613
1987,4975
1987,2369
1987,4316
1987,2595
1987,4621
1987,3294
1987,5425
1987,2698
1987,3032
1988,4974
1988,4234
1988,3744
1988,3128
1988,5382
1988,5048
1988,3852
1988,5237
1988,4665
1988,2003
1988,3197
1988,4162
1988,5511
1988,2893
1988,4243
1988,3557
1988,2523
1988,3234
1988,2099
1988,4970
1990,2761
1990,4602
1990,4265
1990,3281
1990,2628
1990,3613
1990,4535
1990,4844
1990,3263
1990,4470
1990,5424
1990,2632
1990,4972
1990,4285
1991,2025
1991,4014
1991,2462
1991,3669
1991,3109
1991,2722
1991,4921
1991,3405
1991,5142
1991,5532
1991,2685
1991,3074
1991,5102
1991,4693
1991,3726
1991,2364
1991,4697
1991,2575
1991,4876
1991,4313
1991,5410
1991,5906
1992,4814
1992,5170
1992,4877
1992,5315
1992,2143
1993,4616
1993,4845
1995,3211
1995,5554
1995,3367
1995,3226
1995,5675
1995,4759
1995,4183
1995,5729
1996,3665
1996,4761
1996,5093
1996,3762
1996,3242
1996,3789
1996,4185
1996,3760
1996,2249
1996,2917
1996,5145
1996,4749
1996,5763
1996,2230
1996,2713
1996,2015
1996,3099
1996,2644
1996,5785
1996,5855
1996,4253
1997,2075
1997,2812
1997,4938
1997,2878
1997,5110
1997,2353
1997,3173
1997,4745
1997,3721
1997,5453
1997,4247
1997,2387
1997,3693
1997,5297
1997,3368
1997,5033
1997,2779
1997,4303
1997,5061
1997,5485
1997,4227
1997,2739
1997,2351
1998,4583
1998,4789
1998,4333
1998,5479
1998,2736
1998,3768
1998,878
1998,5355
1998,2612
1999,4279
1999,5467
2000,3089
2000,3129
2000,2452
2000,5862
2000,4006
2000,3656
2000,5294
2000,2443
2000,4929
2000,4306
2001,3306
2001,2173
2001,2537
2001,3459
2001,3404
2001,2059
2001,5076
2001,4135
2001,3793
2001,4065
2001,4618
2001,5797
2001,3094
2001,2367
2001,4906
2001,5416
2001,5871
2001,2547
2002,2595
2002,3931
2002,5205
2004,2813
2004,5464
2004,2709
2005,3576
2005,2097
2005,5882
2006,4971
2006,3218
2007,5181
2007,5252
2007,1374
2007,2624
2008,2107
2008,5468
2009,4081
2009,4032
2009,2931
2009,4340
2009,3831
2009,4916
2009,2725
2009,3620
2009,3680
2009,3968
2009,2776
2009,3576
2009,5772
2009,5346
2009,4700
2009,3230
2009,5899
2009,3617
2009,3270
2009,3972
2009,2324
2009,4459
2009,4874
2010,3699
2010,5471
2011,4140
2011,4141
2011,4746
2011,3570
2011,3381
2012,2589
2012,4869
2012,3154
2012,3846
2012,2915
2012,5278
2013,5285
2013,2218
2013,541
2013,5625
2013,5305
2013,4766
2013,4918
2013,4036
2013,5119
2013,3994
2013,2060
2013,2160
2013,845
2013,4325
2013,2920
2013,5615
2013,3530
2013,3325
2013,5543
2013,5900
2013,4296
2013,5692
2013,3983
2013,3239
2015,4915
2015,3170
2015,2792
2015,5202
2015,4185
2015,2998
2015,4749
2015,3789
2015,2230
2015,3140
2015,2713
2015,5817
2015,2524
2015,2899
2015,3099
2015,2337
2015,3377
2015,4761
2015,1368
2015,5093
2015,5695
2015,2063
2015,5369
2015,2730
2015,2639
2015,5263
2015,2260
2015,4680
2015,2647
2015,5855
2015,4498
2015,2554
2015,3665
2015,2555
2015,4501
2015,3762
2015,3242
2015,3309
2015,3489
2015,5683
2015,3684
2015,2759
2015,4598
2015,4047
2015,5785
2015,4253
2015,3588
2015,2644
2015,2600
2015,5885
2015,5800
2015,3883
2015,4727
2015,2249
2015,4537
2015,4911
1285,4908
1285,3864
1285,4395
1285,1285
2018,5457
2018,5325
2018,3676
2018,4114
2018,3395
2018,2495
2018,3374
2018,5161
2019,5261
2019,3295
2019,3008
2020,769
2020,3537
2021,2989
2021,2111
2021,2506
2021,2563
2021,2340
2021,2296
2021,2752
2021,2386
2021,5635
2021,3313
2021,5775
2021,5477
2021,4752
2021,5314
2021,3659
2021,4932
2022,2327
2022,3033
2022,4240
2022,2094
2022,3558
2022,2495
2022,4829
2022,4836
2022,3014
2023,4416
2023,3681
2024,5380
2024,3405
2024,5777
2024,4475
2024,4786
2024,3761
2024,4891
2024,3481
2024,2345
2024,2722
2025,4014
2025,5722
2025,2653
2025,3669
2025,3109
2025,2722
2025,5380
2025,4921
2025,1060
2025,5532
2025,3726
2025,4693
2025,1672
2025,2364
2025,2575
2025,5506
2025,2698
2026,2645
2026,5326
2026,4023
2026,4595
2026,5434
2026,2707
2026,2453
2026,5151
2026,5489
2026,3879
2026,4421
2027,3075
2027,1920
2027,4762
2027,3008
2027,3419
2027,3589
2027,2129
2027,4299
2027,5379
2027,2994
2027,3998
2027,5120
2027,5509
2027,3900
2027,3291
2027,5222
2027,3155
2027,3236
2027,2163
2027,5078
2027,569
2027,3068
2027,2040
2027,3600
2027,4457
2027,4534
2027,4380
2027,3638
2027,5846
2027,3870
2027,5840
2028,4825
2028,3208
2028,4797
2028,5282
2028,2206
2028,3841
2028,2463
2028,2706
2028,5372
2028,2542
2028,5185
2028,2398
2028,4791
2029,2324
2029,3346
2029,2776
2029,3680
2030,4946
2030,1232
2030,2061
2030,2972
2030,3546
2030,2180
2030,5128
2030,5716
2030,3301
2030,5139
2030,3056
2030,5279
2030,3624
2031,3008
2032,2111
2032,5777
2032,5867
2032,2386
2032,2675
2032,5633
2032,5483
2033,2778
2033,3950
2034,3329
2034,541
2034,2948
2034,5557
2034,4183
2034,5357
2034,3367
2034,3305
2034,3506
2034,5678
2034,3609
2034,2072
2034,4815
2034,2565
2034,3768
2034,4789
2034,4193
2034,3226
2034,3955
2034,4900
2034,2634
2034,4005
2034,5479
2034,4883
2034,3094
2034,5321
2034,3696
2034,5055
2034,5729
2034,3117
2034,5705
2034,3211
2034,2612
2034,2736
2034,4759
2035,3305
2035,4333
2036,3539
2036,4061
2036,5602
2036,4597
2037,5078
2037,3008
2038,4551
2038,2760
2038,3833
2038,3826
2038,4140
2038,4466
2038,1333
2038,4746
2038,5832
2038,5644
2038,3608
2038,4353
2038,5842
2038,1700
2039,5018
2039,2094
2039,5126
2039,4484
2039,3950
2039,4829
2039,2654
2039,2306
2039,2641
2040,5670
2040,3008
2040,3102
2040,3870
2041,3076
2041,5176
2041,2988
2041,2791
2041,5421
2041,2515
2041,3363
2041,2173
2041,4643
2041,5638
2041,5726
2041,5277
2041,2320
2041,3536
2041,2619
2041,5797
2041,3281
2041,3110
2041,2723
2041,4857
2041,2629
2041,2445
2041,5213
2041,2551
2041,5042
2041,2929
2041,3848
2041,4418
2041,5137
2041,4878
2041,2562
2041,4135
2041,3041
2041,4799
2041,4427
2041,5868
2041,2761
2041,4602
2041,4972
2041,4660
2041,5069
2041,5072
2041,4525
2041,2770
2041,3793
2041,2593
2041,4160
2041,2298
2041,5701
2041,4901
2041,2690
2041,4622
2041,5441
2041,4470
2041,5520
2042,3387
2042,3918
2042,4555
2042,3518
2042,5118
2042,4917
2042,5236
2043,4274
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================================================
FILE: data/restaurant_edges.csv
================================================
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================================================
FILE: output/restaurant.csv
================================================
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================================================
FILE: src/diffusion_2_vec.py
================================================
"""Diff2Vec model."""

import time
import logging
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
from gensim.models import Word2Vec, Doc2Vec
import numpy.distutils.system_info as sysinfo
from subgraphcomponents import SubGraphComponents
from gensim.models.word2vec import logger, FAST_VERSION
from helper import parameter_parser, result_processing
from helper import process_non_pooled_model_data, argument_printer

sysinfo.get_info("atlas")
logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO)


def create_features(seeding, edge_list_path, vertex_set_cardinality):
    """
    Creating a single feature for every node.
    :param seeding: Random seed.
    :param edge_list_path:  Path to edge list csv.
    :param vertex_set_cardinality: Number of diffusions per node.
    :return: Sequences and measurements.
    """
    sub_graphs = SubGraphComponents(edge_list_path, seeding, vertex_set_cardinality)
    return sub_graphs.paths, sub_graphs.read_time, sub_graphs.generation_time, sub_graphs.counts

def run_parallel_feature_creation(edge_list_path,
                                  vertex_set_card,
                                  replicates,
                                  workers):
    """
    Creating linear node sequences for every node multiple times in a parallel fashion
    :param edge_list_path: Path to edge list csv.
    :param vertex_set_card: Number of diffusions per node.
    :param replicates: Number of unique nodes per diffusion.
    :param workers: Number of cores used.
    :return walk_results: List of 3-length tuples with sequences and performance measurements.
    :return counts: Number of nodes.
    """
    results = Parallel(n_jobs=workers)(delayed(create_features)(i, edge_list_path, vertex_set_card) for i in tqdm(range(replicates)))
    walk_results, counts = result_processing(results)
    return walk_results, counts

def learn_pooled_embeddings(walks, counts, args):
    """
    Method to learn an embedding given the sequences and arguments.
    :param walks: Linear vertex sequences.
    :param counts: Number of nodes.
    :param args: Arguments.
    """
    model = Word2Vec(walks,
                     size=args.dimensions,
                     window=args.window_size,
                     min_count=1,
                     sg=1,
                     workers=args.workers,
                     iter=args.iter,
                     alpha=args.alpha)

    save_embedding(args, model, counts)

def learn_non_pooled_embeddings(walks, counts, args):
    """
    Method to learn an embedding given the sequences and arguments.
    :param walks: Linear vertex sequences.
    :param counts: Number of nodes.
    :param args: Arguments.
    """
    walks = process_non_pooled_model_data(walks, counts, args)
    model = Doc2Vec(walks,
                    size=args.dimensions,
                    window=0,
                    dm=0,
                    alpha=args.alpha,
                    iter=args.iter,
                    workers=args.workers)

    save_embedding(args, model, counts)

def save_embedding(args, model, counts):
    """
    Function to save the embedding.
    :param args: Arguments object.
    :param model: The embedding model object.
    :param counts: Number of nodes.
    """
    out = []
    for node in range(1, counts[0]):
        if args.model == "non-pooled":
            out.append([int(node)-1] + list(model.docvecs[node]))
        else:
            out.append([int(node)-1] + list(model.wv[str(node-1)]))
    columns = ["node"] + ["x_"+str(x) for x in range(args.dimensions)]
    out = pd.DataFrame(out, columns=columns)
    out = out.sort_values(["node"])
    out.to_csv(args.output, index=None)

def main(args):
    """
    Main method for creating sequences and learning the embedding.
    :param args: Arguments object.
    """
    argument_printer(args)
    print("\n----------\nFeature extraction starts.\n----------\n\n")
    walks, counts = run_parallel_feature_creation(args.input,
                                                  args.vertex_set_cardinality,
                                                  args.num_diffusions,
                                                  args.workers)
    print("\n----------\nLearning starts.\n----------\n")

    if args.model == "non-pooled":
        learn_non_pooled_embeddings(walks, counts, args)
    else:
        learn_pooled_embeddings(walks, counts, args)

if __name__ == "__main__":
    args = parameter_parser()
    main(args)


================================================
FILE: src/diffusiontrees.py
================================================
"""Eulerian Diffuser."""

import random
import networkx as nx

class EulerianDiffuser:
    """
    Class to make diffusions for a given graph.
    """
    def __init__(self, graph, number_of_nodes):
        """
        Initializing a diffusion object.
        :param graph: Graph of interest.
        :param number_of_nodes: Cardinality of vertex set.
        """
        self.graph = graph
        self.number_of_nodes = number_of_nodes
        self.nodes = graph.nodes()
        self.run_diffusions()

    def run_diffusion_process(self, node):
        """
        Generating a diffusion tree from a given source node.
        Linearizing it with an Eulerian tour.
        :param node: Source of diffusion.
        :return euler: Eulerian linear node sequence.
        """
        infected = [node]
        sub_graph = nx.DiGraph()
        sub_graph.add_node(node)
        infected_counter = 1
        while infected_counter < self.number_of_nodes:
            end_point = random.sample(infected, 1)[0]
            nebs = [node for node in self.graph.neighbors(end_point)]
            sample = random.sample(nebs, 1)[0]
            if sample not in infected:
                infected_counter = infected_counter + 1
                infected = infected + [sample]
                sub_graph.add_edges_from([(end_point, sample), (sample, end_point)])
                if infected_counter == self.number_of_nodes:
                    break
        euler = [str(u) for u, v in nx.eulerian_circuit(sub_graph, infected[0])]
        if len(euler) == 0:
            euler = [str(u) for u, v in nx.eulerian_circuit(graph, infected[0])]
        return euler

    def run_diffusions(self):
        """
        Running diffusions from every node.
        """
        self.diffusions = {node: self.run_diffusion_process(node) for node in self.nodes}


================================================
FILE: src/helper.py
================================================
"""Helper functions."""

import argparse
import numpy as np
from tqdm import tqdm
from texttable import Texttable
from gensim.models.doc2vec import TaggedDocument

def parameter_parser():
    """
    A method to parse up command line parameters.
    By default it gives an embedding of the Facebook Restaurants network.
    The default hyperparameters give a high quality representation already without grid search.
    :return : Object with hyperparameters.
    """

    parser = argparse.ArgumentParser(description="Run diffusion2vec.")

    parser.add_argument("--input",
                        nargs="?",
                        default="./data/restaurant_edges.csv",
	                help="Input graph path")

    parser.add_argument("--output",
                        nargs="?",
                        default="./output/restaurant.csv",
	                help="Embeddings path")

    parser.add_argument("--model",
                        nargs="?",
                        default="pooled",
	                help="Model type.")

    parser.add_argument("--dimensions",
                        type=int,
                        default=128,
	                help="Number of dimensions. Default is 128.")

    parser.add_argument("--vertex-set-cardinality",
                        type=int,
                        default=40,
	                help="Length of diffusion per source is 2*cardianlity-1. Default is 40.")

    parser.add_argument("--num-diffusions",
                        type=int,
                        default=10,
	                help="Number of diffusions per source. Default is 10.")

    parser.add_argument("--window-size",
                        type=int,
                        default=10,
                    	help="Context size for optimization. Default is 10.")

    parser.add_argument("--iter",
                        default=1,
                        type=int,
                        help="Number of epochs in ASGD. Default is 1.")

    parser.add_argument("--workers",
                        type=int,
                        default=4,
	                help="Number of cores. Default is 4.")

    parser.add_argument("--alpha",
                        type=float,
                        default=0.025,
	                help="Initial learning rate. Default is 0.025.")

    return parser.parse_args()


def argument_printer(args):
    """
    Function to print the arguments in a nice tabular format.
    :param args: Parameters used for the model.
    """
    args = vars(args)
    keys = sorted(args.keys())
    t = Texttable()
    t.add_rows([["Parameter", "Value"]])
    t.add_rows([[k.replace("_", " ").capitalize(), args[k]] for k in keys])
    print(t.draw())

def generation_tab_printer(read_times, generation_times):
    """
    Function to print the time logs in a nice tabular format.
    :param read_times: List of reading times.
    :param generation_times: List of generation times.
    """
    t = Texttable()
    t.add_rows([["Metric", "Value"],
                ["Mean graph read time:", np.mean(read_times)],
                ["Standard deviation of read time.", np.std(read_times)]])
    print(t.draw())
    t = Texttable()
    t.add_rows([["Metric", "Value"],
                ["Mean sequence generation time:", np.mean(generation_times)],
                ["Standard deviation of generation time.", np.std(generation_times)]])
    print(t.draw())

def result_processing(results):
    """
    Function to separate the sequences from time measurements and process them.
    :param results: List of 3-length tuples including the sequences and results.
    :return walk_results: List of random walks.
    :return counts: Number of nodes.
    """
    walk_results = [res[0] for res in results]
    read_time_results = [res[1] for res in results]
    generation_time_results = [res[2] for res in results]
    counts = [res[3] for res in results]
    generation_tab_printer(read_time_results, generation_time_results)
    walk_results = [walk for walks in walk_results for walk in walks]
    return walk_results, counts


def process_non_pooled_model_data(walks, counts, args):
    """
    Function to extract proximity statistics.
    :param walks: Diffusion lists.
    :param counts: Number of nodes.
    :param args: Arguments objects.
    :return docs: Processed walks.
    """
    print("Run feature extraction across windows.")
    features = {str(node): [] for node in range(counts)}
    for walk in tqdm(walks):
        for i in range(len(walk)-args.window_size):
            for j in range(1, args.window_size+1):
                features[walk[i]].append(["+"+str(j)+"_"+walk[i+j]])
                features[walk[i+j]].append(["_"+str(j)+"_"+walk[i]])

    docs = [TaggedDocument(words=[x[0] for x in v], tags=[str(k)]) for k, v in features.items()]
    return docs


================================================
FILE: src/subgraphcomponents.py
================================================
"""Subgraph components module."""

import time
import random
import pandas as pd
import networkx as nx
from diffusiontrees import EulerianDiffuser

class SubGraphComponents:
    """
    Methods separate the original graph and run diffusion on each node in the subgraphs.
    """
    def __init__(self, edge_list_path, seeding, vertex_set_cardinality):
        """
        Initializing the object with the main parameters.
        :param edge_list_path: Path to the csv with edges.
        :param seeding: Random seed.
        :param vertex_set_cardinality: Number of unique nodes per tree.
        """
        self.seed = seeding
        self.vertex_set_cardinality = vertex_set_cardinality
        self.read_start_time = time.time()
        self.graph = nx.from_edgelist(pd.read_csv(edge_list_path, index_col=None).values.tolist())
        self.counts = len(self.graph.nodes())+1
        self.separate_subcomponents()
        self.single_feature_generation_run()

    def separate_subcomponents(self):
        """
        Finding the connected components.
        """
        comps = [self.graph.subgraph(c) for c in nx.connected_components(self.graph)]
        self.graph = sorted(comps, key=len, reverse=True)
        self.read_time = time.time()-self.read_start_time

    def single_feature_generation_run(self):
        """
        Running a round of diffusions and measuring the sequence generation performance.
        """
        random.seed(self.seed)
        self.generation_start_time = time.time()
        self.paths = {}
        for sub_graph in self.graph:
            current_cardinality = len(sub_graph.nodes())
            if current_cardinality < self.vertex_set_cardinality:
                self.vertex_set_cardinality = current_cardinality
            diffuser = EulerianDiffuser(sub_graph, self.vertex_set_cardinality)
            self.paths.update(diffuser.diffusions)
        self.paths = [v for k, v in self.paths.items()]
        self.generation_time = time.time() - self.generation_start_time