|
9 | 9 | }, |
10 | 10 | { |
11 | 11 | "cell_type": "code", |
12 | | - "execution_count": null, |
| 12 | + "execution_count": 1, |
13 | 13 | "metadata": {}, |
14 | 14 | "outputs": [], |
15 | 15 | "source": [ |
|
272 | 272 | " <tr style=\"text-align: right;\">\n", |
273 | 273 | " <th></th>\n", |
274 | 274 | " <th>model_name</th>\n", |
275 | | - " <th>dataset_name</th>\n", |
276 | 275 | " <th>dataset_path</th>\n", |
277 | 276 | " <th>dataset_config</th>\n", |
278 | 277 | " <th>horizon</th>\n", |
279 | | - " <th>cutoff</th>\n", |
280 | | - " <th>lead_time</th>\n", |
281 | | - " <th>min_ts_length</th>\n", |
| 278 | + " <th>num_windows</th>\n", |
| 279 | + " <th>initial_cutoff</th>\n", |
| 280 | + " <th>window_step_size</th>\n", |
| 281 | + " <th>min_context_length</th>\n", |
282 | 282 | " <th>max_context_length</th>\n", |
283 | 283 | " <th>seasonality</th>\n", |
284 | 284 | " <th>...</th>\n", |
285 | | - " <th>multiple_target_columns</th>\n", |
286 | 285 | " <th>past_dynamic_columns</th>\n", |
287 | | - " <th>excluded_columns</th>\n", |
| 286 | + " <th>static_columns</th>\n", |
| 287 | + " <th>task_name</th>\n", |
288 | 288 | " <th>test_error</th>\n", |
289 | 289 | " <th>training_time_s</th>\n", |
290 | 290 | " <th>inference_time_s</th>\n", |
|
297 | 297 | " <tbody>\n", |
298 | 298 | " <tr>\n", |
299 | 299 | " <th>0</th>\n", |
300 | | - " <td>auto_theta</td>\n", |
301 | | - " <td>chronos_datasets_monash_m1_yearly</td>\n", |
| 300 | + " <td>seasonal_naive</td>\n", |
302 | 301 | " <td>autogluon/chronos_datasets</td>\n", |
303 | | - " <td>monash_m1_yearly</td>\n", |
| 302 | + " <td>monash_m1_quarterly</td>\n", |
304 | 303 | " <td>8</td>\n", |
| 304 | + " <td>1</td>\n", |
305 | 305 | " <td>-8</td>\n", |
| 306 | + " <td>8</td>\n", |
306 | 307 | " <td>1</td>\n", |
307 | | - " <td>9</td>\n", |
308 | 308 | " <td>NaN</td>\n", |
309 | | - " <td>1</td>\n", |
| 309 | + " <td>4</td>\n", |
310 | 310 | " <td>...</td>\n", |
311 | | - " <td>NaN</td>\n", |
312 | 311 | " <td>[]</td>\n", |
313 | 312 | " <td>[]</td>\n", |
314 | | - " <td>4.241262</td>\n", |
315 | | - " <td>NaN</td>\n", |
316 | | - " <td>7.116432</td>\n", |
317 | | - " <td>296cc3ca5975e847</td>\n", |
| 313 | + " <td>monash_m1_quarterly</td>\n", |
| 314 | + " <td>2.077537</td>\n", |
| 315 | + " <td>0.0</td>\n", |
| 316 | + " <td>1.687698</td>\n", |
| 317 | + " <td>5dd7170c16393209</td>\n", |
318 | 318 | " <td>False</td>\n", |
319 | | - " <td>0.2.1</td>\n", |
320 | | - " <td>4.241262</td>\n", |
| 319 | + " <td>0.6.0</td>\n", |
| 320 | + " <td>2.077537</td>\n", |
321 | 321 | " </tr>\n", |
322 | 322 | " <tr>\n", |
323 | 323 | " <th>1</th>\n", |
324 | | - " <td>auto_theta</td>\n", |
325 | | - " <td>chronos_datasets_monash_electricity_weekly</td>\n", |
| 324 | + " <td>ets</td>\n", |
326 | 325 | " <td>autogluon/chronos_datasets</td>\n", |
327 | | - " <td>monash_electricity_weekly</td>\n", |
| 326 | + " <td>monash_m1_quarterly</td>\n", |
328 | 327 | " <td>8</td>\n", |
329 | | - " <td>2013-01-01T00:00:00</td>\n", |
330 | 328 | " <td>1</td>\n", |
331 | | - " <td>9</td>\n", |
332 | | - " <td>NaN</td>\n", |
| 329 | + " <td>-8</td>\n", |
| 330 | + " <td>8</td>\n", |
333 | 331 | " <td>1</td>\n", |
334 | | - " <td>...</td>\n", |
335 | 332 | " <td>NaN</td>\n", |
| 333 | + " <td>4</td>\n", |
| 334 | + " <td>...</td>\n", |
336 | 335 | " <td>[]</td>\n", |
337 | 336 | " <td>[]</td>\n", |
338 | | - " <td>1.428428</td>\n", |
339 | | - " <td>NaN</td>\n", |
340 | | - " <td>2.812927</td>\n", |
341 | | - " <td>1bf59473dbf463a3</td>\n", |
| 337 | + " <td>monash_m1_quarterly</td>\n", |
| 338 | + " <td>1.660810</td>\n", |
| 339 | + " <td>0.0</td>\n", |
| 340 | + " <td>4.366176</td>\n", |
| 341 | + " <td>5dd7170c16393209</td>\n", |
342 | 342 | " <td>False</td>\n", |
343 | | - " <td>0.2.1</td>\n", |
344 | | - " <td>1.428428</td>\n", |
| 343 | + " <td>0.6.0</td>\n", |
| 344 | + " <td>1.660810</td>\n", |
345 | 345 | " </tr>\n", |
346 | 346 | " <tr>\n", |
347 | 347 | " <th>2</th>\n", |
348 | | - " <td>auto_theta</td>\n", |
349 | | - " <td>chronos_datasets_monash_electricity_weekly</td>\n", |
| 348 | + " <td>theta</td>\n", |
350 | 349 | " <td>autogluon/chronos_datasets</td>\n", |
351 | | - " <td>monash_electricity_weekly</td>\n", |
| 350 | + " <td>monash_m1_quarterly</td>\n", |
352 | 351 | " <td>8</td>\n", |
353 | | - " <td>2014-01-01T00:00:00</td>\n", |
354 | 352 | " <td>1</td>\n", |
355 | | - " <td>9</td>\n", |
356 | | - " <td>NaN</td>\n", |
| 353 | + " <td>-8</td>\n", |
| 354 | + " <td>8</td>\n", |
357 | 355 | " <td>1</td>\n", |
358 | | - " <td>...</td>\n", |
359 | 356 | " <td>NaN</td>\n", |
| 357 | + " <td>4</td>\n", |
| 358 | + " <td>...</td>\n", |
360 | 359 | " <td>[]</td>\n", |
361 | 360 | " <td>[]</td>\n", |
362 | | - " <td>1.610647</td>\n", |
363 | | - " <td>NaN</td>\n", |
364 | | - " <td>6.573564</td>\n", |
365 | | - " <td>1bf59473dbf463a3</td>\n", |
| 361 | + " <td>monash_m1_quarterly</td>\n", |
| 362 | + " <td>1.705247</td>\n", |
| 363 | + " <td>0.0</td>\n", |
| 364 | + " <td>0.125761</td>\n", |
| 365 | + " <td>5dd7170c16393209</td>\n", |
366 | 366 | " <td>False</td>\n", |
367 | | - " <td>0.2.1</td>\n", |
368 | | - " <td>1.610647</td>\n", |
| 367 | + " <td>0.6.0</td>\n", |
| 368 | + " <td>1.705247</td>\n", |
369 | 369 | " </tr>\n", |
370 | 370 | " <tr>\n", |
371 | 371 | " <th>3</th>\n", |
372 | | - " <td>auto_arima</td>\n", |
373 | | - " <td>chronos_datasets_monash_m1_yearly</td>\n", |
| 372 | + " <td>seasonal_naive</td>\n", |
374 | 373 | " <td>autogluon/chronos_datasets</td>\n", |
375 | | - " <td>monash_m1_yearly</td>\n", |
| 374 | + " <td>monash_electricity_weekly</td>\n", |
| 375 | + " <td>8</td>\n", |
| 376 | + " <td>2</td>\n", |
| 377 | + " <td>-16</td>\n", |
376 | 378 | " <td>8</td>\n", |
377 | | - " <td>-8</td>\n", |
378 | 379 | " <td>1</td>\n", |
379 | | - " <td>9</td>\n", |
380 | 380 | " <td>NaN</td>\n", |
381 | 381 | " <td>1</td>\n", |
382 | 382 | " <td>...</td>\n", |
383 | | - " <td>NaN</td>\n", |
384 | 383 | " <td>[]</td>\n", |
385 | 384 | " <td>[]</td>\n", |
386 | | - " <td>3.993800</td>\n", |
387 | | - " <td>NaN</td>\n", |
388 | | - " <td>8.246975</td>\n", |
389 | | - " <td>296cc3ca5975e847</td>\n", |
| 385 | + " <td>monash_electricity_weekly</td>\n", |
| 386 | + " <td>2.535526</td>\n", |
| 387 | + " <td>0.0</td>\n", |
| 388 | + " <td>1.175560</td>\n", |
| 389 | + " <td>b7cd1c9df3391815</td>\n", |
390 | 390 | " <td>False</td>\n", |
391 | | - " <td>0.2.1</td>\n", |
392 | | - " <td>3.993800</td>\n", |
| 391 | + " <td>0.6.0</td>\n", |
| 392 | + " <td>2.535526</td>\n", |
393 | 393 | " </tr>\n", |
394 | 394 | " <tr>\n", |
395 | 395 | " <th>4</th>\n", |
396 | | - " <td>auto_arima</td>\n", |
397 | | - " <td>chronos_datasets_monash_electricity_weekly</td>\n", |
| 396 | + " <td>ets</td>\n", |
398 | 397 | " <td>autogluon/chronos_datasets</td>\n", |
399 | 398 | " <td>monash_electricity_weekly</td>\n", |
400 | 399 | " <td>8</td>\n", |
401 | | - " <td>2013-01-01T00:00:00</td>\n", |
| 400 | + " <td>2</td>\n", |
| 401 | + " <td>-16</td>\n", |
| 402 | + " <td>8</td>\n", |
402 | 403 | " <td>1</td>\n", |
403 | | - " <td>9</td>\n", |
404 | 404 | " <td>NaN</td>\n", |
405 | 405 | " <td>1</td>\n", |
406 | 406 | " <td>...</td>\n", |
407 | | - " <td>NaN</td>\n", |
408 | 407 | " <td>[]</td>\n", |
409 | 408 | " <td>[]</td>\n", |
410 | | - " <td>1.720373</td>\n", |
411 | | - " <td>NaN</td>\n", |
412 | | - " <td>23.514658</td>\n", |
413 | | - " <td>1bf59473dbf463a3</td>\n", |
| 409 | + " <td>monash_electricity_weekly</td>\n", |
| 410 | + " <td>2.552429</td>\n", |
| 411 | + " <td>0.0</td>\n", |
| 412 | + " <td>3.755289</td>\n", |
| 413 | + " <td>b7cd1c9df3391815</td>\n", |
414 | 414 | " <td>False</td>\n", |
415 | | - " <td>0.2.1</td>\n", |
416 | | - " <td>1.720373</td>\n", |
| 415 | + " <td>0.6.0</td>\n", |
| 416 | + " <td>2.552429</td>\n", |
417 | 417 | " </tr>\n", |
418 | 418 | " </tbody>\n", |
419 | 419 | "</table>\n", |
420 | | - "<p>5 rows × 26 columns</p>\n", |
| 420 | + "<p>5 rows × 28 columns</p>\n", |
421 | 421 | "</div>" |
422 | 422 | ], |
423 | 423 | "text/plain": [ |
424 | | - " model_name dataset_name \\\n", |
425 | | - "0 auto_theta chronos_datasets_monash_m1_yearly \n", |
426 | | - "1 auto_theta chronos_datasets_monash_electricity_weekly \n", |
427 | | - "2 auto_theta chronos_datasets_monash_electricity_weekly \n", |
428 | | - "3 auto_arima chronos_datasets_monash_m1_yearly \n", |
429 | | - "4 auto_arima chronos_datasets_monash_electricity_weekly \n", |
430 | | - "\n", |
431 | | - " dataset_path dataset_config horizon \\\n", |
432 | | - "0 autogluon/chronos_datasets monash_m1_yearly 8 \n", |
433 | | - "1 autogluon/chronos_datasets monash_electricity_weekly 8 \n", |
434 | | - "2 autogluon/chronos_datasets monash_electricity_weekly 8 \n", |
435 | | - "3 autogluon/chronos_datasets monash_m1_yearly 8 \n", |
436 | | - "4 autogluon/chronos_datasets monash_electricity_weekly 8 \n", |
| 424 | + " model_name dataset_path dataset_config \\\n", |
| 425 | + "0 seasonal_naive autogluon/chronos_datasets monash_m1_quarterly \n", |
| 426 | + "1 ets autogluon/chronos_datasets monash_m1_quarterly \n", |
| 427 | + "2 theta autogluon/chronos_datasets monash_m1_quarterly \n", |
| 428 | + "3 seasonal_naive autogluon/chronos_datasets monash_electricity_weekly \n", |
| 429 | + "4 ets autogluon/chronos_datasets monash_electricity_weekly \n", |
437 | 430 | "\n", |
438 | | - " cutoff lead_time min_ts_length max_context_length \\\n", |
439 | | - "0 -8 1 9 NaN \n", |
440 | | - "1 2013-01-01T00:00:00 1 9 NaN \n", |
441 | | - "2 2014-01-01T00:00:00 1 9 NaN \n", |
442 | | - "3 -8 1 9 NaN \n", |
443 | | - "4 2013-01-01T00:00:00 1 9 NaN \n", |
| 431 | + " horizon num_windows initial_cutoff window_step_size min_context_length \\\n", |
| 432 | + "0 8 1 -8 8 1 \n", |
| 433 | + "1 8 1 -8 8 1 \n", |
| 434 | + "2 8 1 -8 8 1 \n", |
| 435 | + "3 8 2 -16 8 1 \n", |
| 436 | + "4 8 2 -16 8 1 \n", |
444 | 437 | "\n", |
445 | | - " seasonality ... multiple_target_columns past_dynamic_columns \\\n", |
446 | | - "0 1 ... NaN [] \n", |
447 | | - "1 1 ... NaN [] \n", |
448 | | - "2 1 ... NaN [] \n", |
449 | | - "3 1 ... NaN [] \n", |
450 | | - "4 1 ... NaN [] \n", |
| 438 | + " max_context_length seasonality ... past_dynamic_columns static_columns \\\n", |
| 439 | + "0 NaN 4 ... [] [] \n", |
| 440 | + "1 NaN 4 ... [] [] \n", |
| 441 | + "2 NaN 4 ... [] [] \n", |
| 442 | + "3 NaN 1 ... [] [] \n", |
| 443 | + "4 NaN 1 ... [] [] \n", |
451 | 444 | "\n", |
452 | | - " excluded_columns test_error training_time_s inference_time_s \\\n", |
453 | | - "0 [] 4.241262 NaN 7.116432 \n", |
454 | | - "1 [] 1.428428 NaN 2.812927 \n", |
455 | | - "2 [] 1.610647 NaN 6.573564 \n", |
456 | | - "3 [] 3.993800 NaN 8.246975 \n", |
457 | | - "4 [] 1.720373 NaN 23.514658 \n", |
| 445 | + " task_name test_error training_time_s inference_time_s \\\n", |
| 446 | + "0 monash_m1_quarterly 2.077537 0.0 1.687698 \n", |
| 447 | + "1 monash_m1_quarterly 1.660810 0.0 4.366176 \n", |
| 448 | + "2 monash_m1_quarterly 1.705247 0.0 0.125761 \n", |
| 449 | + "3 monash_electricity_weekly 2.535526 0.0 1.175560 \n", |
| 450 | + "4 monash_electricity_weekly 2.552429 0.0 3.755289 \n", |
458 | 451 | "\n", |
459 | 452 | " dataset_fingerprint trained_on_this_dataset fev_version MASE \n", |
460 | | - "0 296cc3ca5975e847 False 0.2.1 4.241262 \n", |
461 | | - "1 1bf59473dbf463a3 False 0.2.1 1.428428 \n", |
462 | | - "2 1bf59473dbf463a3 False 0.2.1 1.610647 \n", |
463 | | - "3 296cc3ca5975e847 False 0.2.1 3.993800 \n", |
464 | | - "4 1bf59473dbf463a3 False 0.2.1 1.720373 \n", |
| 453 | + "0 5dd7170c16393209 False 0.6.0 2.077537 \n", |
| 454 | + "1 5dd7170c16393209 False 0.6.0 1.660810 \n", |
| 455 | + "2 5dd7170c16393209 False 0.6.0 1.705247 \n", |
| 456 | + "3 b7cd1c9df3391815 False 0.6.0 2.535526 \n", |
| 457 | + "4 b7cd1c9df3391815 False 0.6.0 2.552429 \n", |
465 | 458 | "\n", |
466 | | - "[5 rows x 26 columns]" |
| 459 | + "[5 rows x 28 columns]" |
467 | 460 | ] |
468 | 461 | }, |
469 | 462 | "execution_count": 11, |
|
483 | 476 | "execution_count": 12, |
484 | 477 | "metadata": {}, |
485 | 478 | "outputs": [ |
486 | | - { |
487 | | - "name": "stderr", |
488 | | - "output_type": "stream", |
489 | | - "text": [ |
490 | | - "/var/folders/dj/hj4wkwks7pd840zxndb25m9w0000gr/T/ipykernel_61496/4135076758.py:2: UserWarning: Columns ['known_dynamic_columns', 'min_context_length', 'static_columns'] are missing from summaries, filling them with None\n", |
491 | | - " fev.leaderboard(summaries, baseline_model=\"seasonal_naive\")\n", |
492 | | - "/var/folders/dj/hj4wkwks7pd840zxndb25m9w0000gr/T/ipykernel_61496/4135076758.py:2: UserWarning: Evaluation summaries contain results from fev < 0.6.0. Results may not be comparable due to breaking changes.\n", |
493 | | - " fev.leaderboard(summaries, baseline_model=\"seasonal_naive\")\n" |
494 | | - ] |
495 | | - }, |
496 | 479 | { |
497 | 480 | "data": { |
498 | 481 | "text/html": [ |
|
533 | 516 | " </thead>\n", |
534 | 517 | " <tbody>\n", |
535 | 518 | " <tr>\n", |
536 | | - " <th>auto_theta</th>\n", |
537 | | - " <td>0.125545</td>\n", |
538 | | - " <td>0.666667</td>\n", |
539 | | - " <td>NaN</td>\n", |
540 | | - " <td>6.573564</td>\n", |
| 519 | + " <th>ets</th>\n", |
| 520 | + " <td>0.133483</td>\n", |
| 521 | + " <td>0.833333</td>\n", |
541 | 522 | " <td>0.0</td>\n", |
542 | | - " <td>0</td>\n", |
543 | | - " </tr>\n", |
544 | | - " <tr>\n", |
545 | | - " <th>auto_arima</th>\n", |
546 | | - " <td>0.112664</td>\n", |
547 | | - " <td>0.666667</td>\n", |
548 | | - " <td>NaN</td>\n", |
549 | | - " <td>23.514658</td>\n", |
| 523 | + " <td>3.755289</td>\n", |
550 | 524 | " <td>0.0</td>\n", |
551 | 525 | " <td>0</td>\n", |
552 | 526 | " </tr>\n", |
553 | 527 | " <tr>\n", |
554 | | - " <th>auto_ets</th>\n", |
555 | | - " <td>0.048807</td>\n", |
556 | | - " <td>0.444444</td>\n", |
557 | | - " <td>NaN</td>\n", |
558 | | - " <td>0.741776</td>\n", |
| 528 | + " <th>theta</th>\n", |
| 529 | + " <td>0.105932</td>\n", |
| 530 | + " <td>0.333333</td>\n", |
| 531 | + " <td>0.0</td>\n", |
| 532 | + " <td>0.125761</td>\n", |
559 | 533 | " <td>0.0</td>\n", |
560 | 534 | " <td>0</td>\n", |
561 | 535 | " </tr>\n", |
562 | 536 | " <tr>\n", |
563 | 537 | " <th>seasonal_naive</th>\n", |
564 | 538 | " <td>0.000000</td>\n", |
565 | | - " <td>0.222222</td>\n", |
566 | | - " <td>NaN</td>\n", |
567 | | - " <td>0.004139</td>\n", |
| 539 | + " <td>0.333333</td>\n", |
| 540 | + " <td>0.0</td>\n", |
| 541 | + " <td>1.444558</td>\n", |
568 | 542 | " <td>0.0</td>\n", |
569 | 543 | " <td>0</td>\n", |
570 | 544 | " </tr>\n", |
|
575 | 549 | "text/plain": [ |
576 | 550 | " skill_score win_rate median_training_time_s \\\n", |
577 | 551 | "model_name \n", |
578 | | - "auto_theta 0.125545 0.666667 NaN \n", |
579 | | - "auto_arima 0.112664 0.666667 NaN \n", |
580 | | - "auto_ets 0.048807 0.444444 NaN \n", |
581 | | - "seasonal_naive 0.000000 0.222222 NaN \n", |
| 552 | + "ets 0.133483 0.833333 0.0 \n", |
| 553 | + "theta 0.105932 0.333333 0.0 \n", |
| 554 | + "seasonal_naive 0.000000 0.333333 0.0 \n", |
582 | 555 | "\n", |
583 | 556 | " median_inference_time_s training_corpus_overlap num_failures \n", |
584 | 557 | "model_name \n", |
585 | | - "auto_theta 6.573564 0.0 0 \n", |
586 | | - "auto_arima 23.514658 0.0 0 \n", |
587 | | - "auto_ets 0.741776 0.0 0 \n", |
588 | | - "seasonal_naive 0.004139 0.0 0 " |
| 558 | + "ets 3.755289 0.0 0 \n", |
| 559 | + "theta 0.125761 0.0 0 \n", |
| 560 | + "seasonal_naive 1.444558 0.0 0 " |
589 | 561 | ] |
590 | 562 | }, |
591 | 563 | "execution_count": 12, |
|
0 commit comments