/
TMDDemo.java
644 lines (554 loc) · 26.6 KB
/
TMDDemo.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
package jtest;
import static tech.v3.Clj.*;
import static tech.v3.TMD.*;
import tech.v3.dataset.Rolling;
import tech.v3.dataset.Modelling;
import tech.v3.dataset.Reductions;
import tech.v3.libs.Arrow;
import tech.v3.libs.Parquet;
import tech.v3.DType; //access to clone method
import static tech.v3.DType.*;
import tech.v3.datatype.Pred;
import tech.v3.datatype.VecMath;
import tech.v3.datatype.Stats;
import tech.v3.datatype.Buffer;
import tech.v3.libs.Nippy;
import tech.v3.datatype.IFnDef;
//Fast map creation when you know you will have to create many maps.
import tech.v3.dataset.FastStruct;
import clojure.lang.RT;
import clojure.lang.IFn;
import java.util.Map;
import java.util.function.Function;
//Imports for the advanced reduction example at the end.
import java.util.HashMap;
import java.util.ArrayList;
import java.util.List;
import java.util.stream.StreamSupport;
import java.util.function.BiFunction;
import java.util.function.BiConsumer;
import java.time.LocalDate;
import java.time.YearMonth;
import java.util.Random;
public class TMDDemo {
public static void main(String[] args) {
println("Loading/compiling library code. Time here can be mitigated with a precompilation step.");
//Front-loading requires so when the code starts to run everyting is compiled.
//For precompilation see tech.v3.Clj.compile.
require("tech.v3.dataset");
require("tech.v3.dataset.neanderthal");
println("Compilation finished.");
//Make dataset can take a string, inputStream, a sequence of maps or a map of columns with
//the map of columns being the most efficient.
//Default file formats:
//csv, tsv, csv.gz, tsv.gz, (compressed, general, and surprisingly fast) .nippy
Map ds = makeDataset("https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv");
println(head(ds));
// https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv [5 3]:
// | symbol | date | price |
// |--------|------------|------:|
// | MSFT | 2000-01-01 | 39.81 |
// | MSFT | 2000-02-01 | 36.35 |
// | MSFT | 2000-03-01 | 43.22 |
// | MSFT | 2000-04-01 | 28.37 |
// | MSFT | 2000-05-01 | 25.45 |
println(head(sortByColumn(ds, "date")));
// https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv [5 3]:
// | symbol | date | price |
// |--------|------------|-------:|
// | AAPL | 2000-01-01 | 25.94 |
// | IBM | 2000-01-01 | 100.52 |
// | MSFT | 2000-01-01 | 39.81 |
// | AMZN | 2000-01-01 | 64.56 |
// | AAPL | 2000-02-01 | 28.66 |
println(ds.get("date"));
// #tech.v3.dataset.column<packed-local-date>[560]
// date
// [2000-01-01, 2000-02-01, 2000-03-01, 2000-04-01, 2000-05-01, 2000-06-01, 2000-07-01, 2000-08-01, 2000-09-01, 2000-10-01, 2000-11-01, 2000-12-01, 2001-01-01, 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 2001-06-01, 2001-07-01, 2001-08-01...]
Object priceCol = ds.get("price");
println("first value:", call(priceCol, 0), ", last value:", call(priceCol, -1));
//first value: 39.81 , last value: 223.02
Map colmapDs = makeDataset(hashmap("a", range(10),
"b", toDoubleArray(range(9,-1,-1))),
hashmap(kw("dataset-name"), "testds"));
println(colmapDs);
// testds [10 2]:
// | b | a |
// |----:|---:|
// | 9.0 | 0 |
// | 8.0 | 1 |
// | 7.0 | 2 |
// | 6.0 | 3 |
// | 5.0 | 4 |
// | 4.0 | 5 |
// | 3.0 | 6 |
// | 2.0 | 7 |
// | 1.0 | 8 |
// | 0.0 | 9 |
println(meta(colmapDs));
// {:name testds}
//It is also trivial to add a virtual column by instantiating a Buffer object
//One thing to note is that colmapDs itself wasn't changed. Assoc create a new
//dataset that shared the unchanged portions with the original dataset
println(assoc(colmapDs, "c", new tech.v3.datatype.LongReader() {
public long lsize() { return 10; }
public long readLong( long idx) {
return 2*idx;
}
}));
//testds [5 3]:
//| b | a | c |
//|----:|---:|---:|
//| 9.0 | 0 | 0 |
//| 8.0 | 1 | 2 |
//| 7.0 | 2 | 4 |
//| 6.0 | 3 | 6 |
//| 5.0 | 4 | 8 |
// The metadata on columns has quite a bit of useful informatio in it.
println(meta(call(colmapDs, "a")), meta(call(colmapDs, "b")));
// {:name a, :datatype :int64, :n-elems 10} {:name b, :datatype :float64, :n-elems 10}
Buffer rows = rows(colmapDs);
println("First row:", call(rows,0), ", last row:", call(rows,-1));
// First row: {b 9.0, a 0} , last row: {b 0.0, a 9}
Buffer rowvecs = rowvecs(colmapDs);
println("First rowvec:", call(rowvecs,0), ", last rowvec:", call(rowvecs,-1));
// First rowvec: [9.0 0] , last rowvec: [0.0 9]
println("Tensor format:", toTensor(colmapDs));
// Tensor format: #tech.v3.tensor<float64>[10 2]
// [[9.000 0.000]
// [8.000 1.000]
// [7.000 2.000]
// [6.000 3.000]
// [5.000 4.000]
// [4.000 5.000]
// [3.000 6.000]
// [2.000 7.000]
// [1.000 8.000]
// [0.000 9.000]]
println("Neanderthal format:", toNeanderthal(colmapDs));
//Neanderthal format: #RealGEMatrix[double, mxn:10x2, layout:column, offset:0]
// ▥ ↓ ↓ ┓
// → 9.00 0.00
// → 8.00 1.00
// → ⁙ ⁙
// → 1.00 8.00
// → 0.00 9.00
// ┗ ┛
Map stocks = makeDataset("https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv");
//Filtering by a column is faster than the generalized row-by-row filter
//and it allows us to make an assumption that if the predicate is a constant
println(head(filterColumn(stocks, "symbol", Pred.eq("MSFT"))));
//https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv [5 3]:
//| symbol | date | price |
//|--------|------------|------:|
//| MSFT | 2000-01-01 | 39.81 |
//| MSFT | 2000-02-01 | 36.35 |
//| MSFT | 2000-03-01 | 43.22 |
//| MSFT | 2000-04-01 | 28.37 |
//| MSFT | 2000-05-01 | 25.45 |
//Grouping returns a map of key to dataset. This can serve as a pre-aggregation
//step or as a simple index.
Map bySymbol = groupByColumn(stocks, "symbol");
println(keys(bySymbol));
//(MSFT AMZN IBM GOOG AAPL)
//Construct a new dataset by scanning a sequence of maps. This performs the aggregation
//step after grouping by symbol. There is a higher performance way of doing this
//described later but this method is most likely sufficient for many many use
//cases.
println(makeDataset(map(new IFnDef() {
public Object invoke(Object kv) {
Map.Entry item = (Map.Entry)kv;
return hashmap("symbol", item.getKey(),
"meanPrice", Stats.mean(column(item.getValue(), "price")));
}}, bySymbol)));
// _unnamed [5 2]:
//| symbol | meanPrice |
//|--------|-------------:|
//| MSFT | 24.73674797 |
//| AMZN | 47.98707317 |
//| IBM | 91.26121951 |
//| GOOG | 415.87044118 |
//| AAPL | 64.73048780 |
//Variable rolling window reductions require the target column to be monotonically
//increasing - for each val x(n), x(n+1) is greater or equal. So for financial data
//this usually means ordered by date.
Map goog = sortByColumn(bySymbol.get("GOOG"), "date");
println(head(goog));
//GOOG [5 3]:
//| symbol | date | price |
//|--------|------------|-------:|
//| GOOG | 2004-08-01 | 102.37 |
//| GOOG | 2004-09-01 | 129.60 |
//| GOOG | 2004-10-01 | 190.64 |
//| GOOG | 2004-11-01 | 181.98 |
//| GOOG | 2004-12-01 | 192.79 |
//If we want our column of dates to be in epoch-days which is a lot more friendly to
//machine learning we can easily do so:
Buffer dateBuf = toBuffer(column(goog, "date"));
//There are many ways to do this but here is a low-level way
println(head(assoc(goog, "date",
//all integer types funnel through LongBuffer/LongReader pathways.
new tech.v3.datatype.LongReader() {
//Aside from :int32, kw("epoch-days") is another valid datatype for
//precisely this data.
public Object elemwiseDatatype() { return int32; }
public long lsize() { return dateBuf.lsize(); }
public long readLong(long idx) {
LocalDate ld = (LocalDate)dateBuf.readObject(idx);
//Missing values will be null when using the readObject pathway.
//The stocks dataset has no missing values. We strongly encourage
//you to deal with missing values before getting into your
//pipeline processing pathways.
return ld.toEpochDay();
}
})));
//GOOG [5 3]:
//| symbol | date | price |
//|--------|------:|-------:|
//| GOOG | 12631 | 102.37 |
//| GOOG | 12662 | 129.60 |
//| GOOG | 12692 | 190.64 |
//| GOOG | 12723 | 181.98 |
//| GOOG | 12753 | 192.79 |
Map variableWin = Rolling.rolling(goog,
Rolling.variableWindow("date", 3, kw("months")),
hashmap("price-mean-3m", Rolling.mean("price"),
"price-max-3m", Rolling.max("price"),
"price-min-3m", Rolling.min("price")));
println(head(variableWin, 10));
//GOOG [10 6]:
//| symbol | date | price | price-max-3m | price-mean-3m | price-min-3m |
//|--------|------------|-------:|-------------:|--------------:|-------------:|
//| GOOG | 2004-08-01 | 102.37 | 190.64 | 140.87000000 | 102.37 |
//| GOOG | 2004-09-01 | 129.60 | 190.64 | 167.40666667 | 129.60 |
//| GOOG | 2004-10-01 | 190.64 | 192.79 | 188.47000000 | 181.98 |
//| GOOG | 2004-11-01 | 181.98 | 195.62 | 190.13000000 | 181.98 |
//| GOOG | 2004-12-01 | 192.79 | 195.62 | 192.13333333 | 187.99 |
//| GOOG | 2005-01-01 | 195.62 | 195.62 | 188.04000000 | 180.51 |
//Create a vector from 0->6*PI in 90 increments.
Object radians = VecMath.mul(2.0*Math.PI, VecMath.div(range(33), 32.0));
Map sinds = makeDataset(hashmap("radians", radians, "sin", VecMath.sin(radians)));
Map fixedWin = Rolling.rolling(sinds,
Rolling.fixedWindow(4),
hashmap("sin-roll-mean", Rolling.mean("sin"),
"sin-roll-max", Rolling.max("sin"),
"sin-roll-min", Rolling.min("sin")));
println(head(fixedWin, 8));
//_unnamed [8 5]:
//| sin | radians | sin-roll-max | sin-roll-min | sin-roll-mean |
//|-----------:|-----------:|-------------:|-------------:|--------------:|
//| 0.00000000 | 0.00000000 | 0.19509032 | 0.00000000 | 0.04877258 |
//| 0.19509032 | 0.19634954 | 0.38268343 | 0.00000000 | 0.14444344 |
//| 0.38268343 | 0.39269908 | 0.55557023 | 0.00000000 | 0.28333600 |
//| 0.55557023 | 0.58904862 | 0.70710678 | 0.19509032 | 0.46011269 |
//| 0.70710678 | 0.78539816 | 0.83146961 | 0.38268343 | 0.61920751 |
//| 0.83146961 | 0.98174770 | 0.92387953 | 0.55557023 | 0.75450654 |
//| 0.92387953 | 1.17809725 | 0.98078528 | 0.70710678 | 0.86081030 |
//| 0.98078528 | 1.37444679 | 1.00000000 | 0.83146961 | 0.93403361 |
//Join algorithm is a fast in-memory hash-based join
Map dsa = makeDataset(hashmap("a", vector("a", "b", "b", "a", "c"),
"b", range(5),
"c", range(5)));
println(dsa);
//_unnamed [5 3]:
//| a | b | c |
//|---|--:|--:|
//| a | 0 | 0 |
//| b | 1 | 1 |
//| b | 2 | 2 |
//| a | 3 | 3 |
//| c | 4 | 4 |
Map dsb = makeDataset(hashmap("a", vector("a", "b", "a", "b", "d"),
"b", range(5),
"c", range(6,11)));
println(dsb);
//_unnamed [5 3]:
//| a | b | c |
//|---|--:|---:|
//| a | 0 | 6 |
//| b | 1 | 7 |
//| a | 2 | 8 |
//| b | 3 | 9 |
//| d | 4 | 10 |
//Join on the columns a,b. Default join mode is inner
println(join(dsa, dsb, hashmap(kw("on"), vector("a", "b"))));
//inner-join [2 4]:
//| a | b | c | right.c |
//|---|--:|--:|--------:|
//| a | 0 | 0 | 6 |
//| b | 1 | 1 | 7 |
//Single column join doesn't require column names wrapped in vectors
println(join(dsa, dsb, hashmap(kw("on"), "a")));
//inner-join [8 5]:
//| a | b | c | right.b | right.c |
//|---|--:|--:|--------:|--------:|
//| a | 0 | 0 | 0 | 6 |
//| a | 3 | 3 | 0 | 6 |
//| b | 1 | 1 | 1 | 7 |
//| b | 2 | 2 | 1 | 7 |
//| a | 0 | 0 | 2 | 8 |
//| a | 3 | 3 | 2 | 8 |
//| b | 1 | 1 | 3 | 9 |
//| b | 2 | 2 | 3 | 9 |
//Outer join on same columns
println(join(dsa, dsb, hashmap(kw("on"), vector("a", "b"),
kw("how"), kw("outer"))));
//outer-join [8 4]:
//| a | b | c | right.c |
//|---|--:|--:|--------:|
//| a | 0 | 0 | 6 |
//| b | 1 | 1 | 7 |
//| b | 2 | 2 | |
//| a | 3 | 3 | |
//| c | 4 | 4 | |
//| a | 2 | | 8 |
//| b | 3 | | 9 |
//| d | 4 | | 10 |
//Specific to timeseries-type information, there is a special join operator
//named leftJoinAsof where every column of the left dataset is represented and it is
//matched with the 'nearest' of a column of the right dataset.
Map targetPrices = makeDataset(hashmap("price", new Double[] { 200.0, 300.0, 400.0 }));
println(leftJoinAsof("price", targetPrices, goog, hashmap(kw("asof-op"), kw("<="))));
//asof-<= [3 4]:
//| price | symbol | date | GOOG.price |
//|------:|--------|------------|-----------:|
//| 200.0 | GOOG | 2005-04-01 | 220.00 |
//| 300.0 | GOOG | 2008-12-01 | 307.65 |
//| 400.0 | GOOG | 2008-09-01 | 400.52 |
println(leftJoinAsof("price", targetPrices, goog, hashmap(kw("asof-op"), kw(">"))));
//asof-> [3 4]:
//| price | symbol | date | GOOG.price |
//|------:|--------|------------|-----------:|
//| 200.0 | GOOG | 2005-01-01 | 195.62 |
//| 300.0 | GOOG | 2005-06-01 | 294.15 |
//| 400.0 | GOOG | 2009-04-01 | 395.97 |
//tech.v3.dataset.Modelling moves us more into machine learning pathways
//We can do things like PCA transformations or train/test pathways.
Object categoricalFit = Modelling.fitCategorical(stocks, "symbol");
println(head(Modelling.transformCategorical(stocks, categoricalFit)));
//https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv [5 3]:
//| symbol | date | price |
//|-------:|------------|-------:|
//| 1.0 | 2000-01-01 | 25.94 |
//| 4.0 | 2000-01-01 | 100.52 |
//| 3.0 | 2000-01-01 | 39.81 |
//| 2.0 | 2000-01-01 | 64.56 |
//| 1.0 | 2000-02-01 | 28.66 |
//Remember the rolling sinewave dataset from before?
//let's run PCA on the dataset.
//This pathway will use the slightly slow covariance based method that has the distinct
//advantage of producing accurate variances in the eigenvalues member.
Object pcaFit = Modelling.fitPCA(fixedWin, hashmap(kw("n-components"), 2));
println(head(Modelling.transformPCA(fixedWin, pcaFit)));
//_unnamed [5 2]:
//| 0 | 1 |
//|------------:|------------:|
//| -2.68909118 | -1.63147765 |
//| -2.65664577 | -1.31993055 |
//| -2.63001624 | -0.99954776 |
//| -2.65746329 | -0.60134499 |
//| -2.66466548 | -0.23414574 |
//We can save out pipeline data alltogether into a byte array using the Nippy namespace.
byte[] data = Nippy.freeze(hashmap("catFit", categoricalFit, "pcaFit", pcaFit));
println("pipeline data byte length:", data.length);
//pipeline data byte length: 864
//We can serialize *just* datasets to arrow which gives us an interesting possibility.
Arrow.datasetToStream(stocks, "test.arrow", null);
//We can mmap them back. This step will fail if you are on an m-1 mac unless you add
//the memory module. See deps.clj for example command line.
try(AutoCloseable resCtx = stackResourceContext()) {
//This dataset is loaded in-place. This means that aside from string tables
//the columns are just loaded from the mmap pointers.
Map mmapds = Arrow.streamToDataset("test.arrow", hashmap(kw("open-type"), kw("mmap")));
println(head(mmapds));
//test.arrow [5 3]:
//| symbol | date | price |
//|--------|------------|-------:|
//| AAPL | 2000-01-01 | 25.94 |
//| IBM | 2000-01-01 | 100.52 |
//| MSFT | 2000-01-01 | 39.81 |
//| AMZN | 2000-01-01 | 64.56 |
//| AAPL | 2000-02-01 | 28.66 |
//Cloning a dataset serves to both realize any lazy columns
//and copy the dataset into jvm-heap memory thus allowing you to return
//something from the stack resource context.
println(head(tech.v3.DType.clone(mmapds)));
}
catch(Exception e){
println(e);
e.printStackTrace(System.out);
}
//Finally we can load/safe to parquet if that is your thing.
Parquet.datasetToParquet(stocks, "test.parquet", null);
//Specifying a subset of columns to load makes this *much* faster.
//To do this use :column-whitelist - see dataset api docs for `->dataset`.
//NOTE - If you don't disable debug logging then serializing to/from parquet is
//unreasonably slow. See logging section of https://techascent.github.io/tech.ml.dataset/tech.v3.libs.parquet.html.
println(head(Parquet.parquetToDataset("test.parquet", null)));
//_unnamed [5 3]:
//| symbol | date | price |
//|--------|------------|-------:|
//| AAPL | 2000-01-01 | 25.94 |
//| IBM | 2000-01-01 | 100.52 |
//| MSFT | 2000-01-01 | 39.81 |
//| AMZN | 2000-01-01 | 64.56 |
//| AAPL | 2000-02-01 | 28.66 |
//Here is a somewhat advanced example. We have a dataset composed of events where each
//row has a start,end date. We want to tally information based the days per a given month
//that the event happened which means we need to expand the dataset into days then reduce
//it to tally over months. Finally we do another crosswise summation to pull out statistics
//based on row information in the dataset.
int nSims = 100;
int nPlacements = 50;
int nExpansion = 20;
long nRows = 1000000;
LocalDate today = LocalDate.now();
Random rand = new Random();
Object startDates = vec(repeatedly(nRows, new IFnDef() { public Object invoke() { return today.minusDays(400 + rand.nextInt(100)); } }));
//Dataset with 1 million rows
Map srcds = makeDataset(hashmap("simulation", repeatedly(nRows, new IFnDef() { public Object invoke() { return rand.nextInt(nSims); }}),
"placement", repeatedly(nRows, new IFnDef() { public Object invoke() { return rand.nextInt(nPlacements); }}),
"start", startDates,
"end", map(new IFnDef() { public Object invoke(Object sd) { return ((LocalDate)sd).plusDays(rand.nextInt(nExpansion)); }},
startDates)));
println(head(srcds));
//_unnamed [5 4]:
//| placement | start | simulation | end |
//|-----------:|------------|------------:|------------|
//| 14 | 2020-09-28 | 86 | 2020-09-29 |
//| 32 | 2020-12-17 | 20 | 2021-01-03 |
//| 23 | 2020-10-15 | 37 | 2020-10-24 |
//| 49 | 2020-10-07 | 18 | 2020-10-22 |
//| 6 | 2020-12-08 | 48 | 2020-12-08 |
//We are going to be creating a lot of these.
IFn mapFact = mapFactory(vector("year-month", "count"));
//We want to produce map of yearmonth to day counts.
BiFunction<YearMonth,Long,Long> incrementor = new BiFunction<YearMonth,Long,Long>() {
public Long apply(YearMonth k, Long v) {
if (v != null) {
return ((long)v) + 1;
} else {
return 1L;
}
}
};
//Tally the days between start/end, record in map of yearMonth to day tally
//Returns a list of maps of "year-month", "count".
IFn tallyDays = new IFnDef() {
public Object invoke(Object row) {
Map rowMap = (Map) row;
LocalDate sd = (LocalDate)rowMap.get("start");
LocalDate ed = (LocalDate)rowMap.get("end");
long ndays = sd.until(ed, java.time.temporal.ChronoUnit.DAYS);
HashMap<YearMonth,Long> tally = new HashMap<YearMonth,Long>();
for (long idx = 0; idx < ndays; ++idx) {
LocalDate cur = sd.plusDays(idx);
YearMonth rm = YearMonth.from(cur);
tally.compute(rm, incrementor);
}
ArrayList<Map> retval = new ArrayList<Map>(tally.size());
tally.forEach(new BiConsumer<YearMonth,Long>() {
public void accept(YearMonth k, Long v) {
retval.add((Map)mapFact.invoke(k, v));
}
});
return retval;
}
};
println(vec(tallyDays.invoke(hashmap("start", LocalDate.parse("2020-12-17"),
"end", LocalDate.parse("2021-01-03")))));
//[{year-month #object[java.time.YearMonth 0x5eafef3a 2020-12], count 15} {year-month #object[java.time.YearMonth 0x3bcfebf6 2021-01], count 2}]
//Next we expand our original dataset to be year-month tallies in addition to
//to start/end dates.
println(rowMapcat(head(srcds), tallyDays, null));
//_unnamed [7 6]:
//| placement | start | simulation | end | count | year-month |
//|----------:|------------|-----------:|------------|------:|------------|
//| 11 | 2020-10-29 | 41 | 2020-11-02 | 1 | 2020-11 |
//| 11 | 2020-10-29 | 41 | 2020-11-02 | 3 | 2020-10 |
//| 13 | 2020-10-11 | 5 | 2020-10-19 | 8 | 2020-10 |
//| 16 | 2020-12-08 | 10 | 2020-12-11 | 3 | 2020-12 |
//| 1 | 2020-10-15 | 52 | 2020-10-19 | 4 | 2020-10 |
//Begin parallelized expansion
Iterable dsSeq = (Iterable)rowMapcat(srcds, tallyDays, hashmap(kw("result-type"), kw("as-seq")));
//The first aggregation is to summarize by placement and simulation the year-month tallies.
//We are essentially replacing count with a summarized count. After this statement
//we can guarantee that the dataset has unique tuples of [simulation, placement, year-month]
Map initAgg = Reductions.groupByColumnsAgg(dsSeq, vector("simulation", "placement", "year-month"),
hashmap("count", Reductions.sum("count")),
null);
println(head(initAgg));
//["simulation" "placement" "year-month"]-aggregation [5 4]:
//| simulation | placement | year-month | count |
//|-----------:|----------:|------------|------:|
//| 0 | 0 | 2020-12 | 622.0 |
//| 0 | 1 | 2020-12 | 591.0 |
//| 0 | 2 | 2020-12 | 500.0 |
//| 0 | 3 | 2020-12 | 549.0 |
//| 0 | 4 | 2020-12 | 595.0 |
// The second aggregation allows us to build of statistics over each placement/year-month
// pair thus finding out the distribution of a given placement, year-month across simluations
Map result = Reductions.groupByColumnsAgg(vector(initAgg), vector("placement", "year-month"),
hashmap("min-count", Reductions.probQuantile("count", 0.0),
"low-95-count", Reductions.probQuantile("count", 0.05),
"q1-count", Reductions.probQuantile("count", 0.25),
"median-count", Reductions.probQuantile("count", 0.5),
"q3-count", Reductions.probQuantile("count", 0.75),
"high-95-count", Reductions.probQuantile("count", 0.95),
"max-count", Reductions.probQuantile("count", 1.0),
"count", Reductions.sum("count")),
null);
//Take a million row dataset, expand it, then perform two grouping aggregations.
println(head(result));
//["placement" "year-month"]-aggregation [5 10]:
//| q3-count | median-count | min-count | high-95-count | placement | max-count | count | low-95-count | q1-count | year-month |
//|---------:|-------------:|----------:|--------------:|----------:|----------:|--------:|-------------:|---------:|------------|
//| 646.0 | 593.0 | 366.0 | 716.0 | 36 | 809.0 | 58920.0 | 475.0 | 536.0 | 2020-12 |
//| 621.0 | 560.0 | 376.0 | 739.0 | 36 | 782.0 | 57107.0 | 459.0 | 512.0 | 2020-10 |
//| 168.0 | 139.0 | 25.0 | 211.0 | 0 | 246.0 | 13875.0 | 76.0 | 112.0 | 2021-01 |
//| 658.0 | 607.0 | 384.0 | 745.0 | 0 | 825.0 | 60848.0 | 486.0 | 561.0 | 2020-12 |
//| 628.0 | 581.0 | 422.0 | 693.0 | 0 | 802.0 | 58148.0 | 468.0 | 539.0 | 2020-11 |
//Let's do a quick file size comparison of the original simulation dataset.
//We have four columns, placement simulation startdate enddate. We know, however,
//that placement and simulation will fit into byte data as they are integers 0-49 and 0-99,
//respectively. So let's start there.
Map simds = (Map)assoc(srcds,
//These are checked casts.
"simulation", makeContainer(kw("uint8"), srcds.get("simulation")),
"placement", makeContainer(kw("uint8"), srcds.get("placement")));
writeDataset(simds, "simulation.csv.gz");
writeDataset(simds, "simulation.nippy");
Arrow.datasetToStream(simds, "simulation.arrow", null);
Arrow.datasetToStream(simds, "simulation-compressed.arrow", hashmap(kw("compression"),
hashmap(kw("compression-type"), kw("zstd"),
kw("level"), 8)));
Parquet.datasetToParquet(simds, "simulation.parquet", null);
IFn fileLen = new IFnDef() {
public Object invoke(Object fname) {
return new java.io.File(str(fname)).length();
}
};
println(makeDataset(vector(hashmap("file-type", "gzipped csv",
"length", fileLen.invoke("simulation.csv.gz")),
hashmap("file-type", "nippy",
"length", fileLen.invoke("simulation.nippy")),
hashmap("file-type", "arrow file",
"length", fileLen.invoke("simulation.arrow")),
hashmap("file-type", "arrow file compressed",
"length", fileLen.invoke("simulation-compressed.arrow")),
hashmap("file-type", "parquet",
"length", fileLen.invoke("simulation.parquet")))));
// _unnamed [5 2]:
//| file-type | length |
//|-----------------------|---------:|
//| gzipped csv | 5903963 |
//| nippy | 5688556 |
//| arrow file | 10501378 |
//| arrow file compressed | 3869554 |
//| parquet | 3396383 |
// If we load clojure.core.async - which neanderthal does - or we use
// clojure.core/pmap then we have to shutdown agents else we get a 1 minute hang
// on shutdown.
shutdownAgents();
}
}