Hello,
I have monthly, sales data for 16 different locations. I built a ‘prophet-like’ model based on @MBrouns video here ( Hierarchical Time Series With Prophet and PyMC3 (Matthijs Brouns) - YouTube
That stated, the model runs fine until I try to make it a hierarchical model with locations. Below is the model code.
location_idx, locations = pd.factorize(df_sat['location'])
coords_ = {'locations':locations,
'obs_id':np.arange(len(location_idx))}
with pm.Model(coords = coords_) as model:
location_idx_ = pm.ConstantData('location_idx', location_idx, dims = 'obs_id')
mu_k = pm.Normal('mu_k', 0, 1)
sigma_k = pm.HalfCauchy('sigma_k', 5)
k = pm.Normal('k', mu = mu_k, sigma = sigma_k, dims = 'locations')
mu_m = pm.Normal('mu_m,', 0, 1)
sigma_m = pm.HalfCauchy('sigma_m', 5)
m = pm.Normal('m', mu=mu_m, sigma = sigma_m, dims = 'locations')
delta = pm.Laplace('delta', 0, 0.1, shape=n_changepoints, dims = 'locations')
growth = k + at.dot(a, delta)
offset = (m + at.dot(a, -s * delta))
trend = pm.Deterministic('trend', growth[location_idx_] * t[location_idx_] + offset[location_idx_], dims = 'locations')
yearly_mu = pm.Normal('yearly_mu', 0, 1)
yearly_sigma = pm.HalfCauchy('yearly_sigma', 0, 1)
yearly_beta = pm.Normal('yearly_beta', mu = yearly_mu, sigma = yearly_sigma, shape = n_components*2, dims = 'locations')
yearly_seasonality = pm.Deterministic('yearly_seasonality',at.dot(yearly_X(t, 365.25/len(t))), dims = 'locations')
monthly_mu = pm.Normal('monthly_mu', 0, 1)
monthly_sigma = pm.HalfCauchy('monthly_sigma', 0, 1)
monthly_beta = pm.Normal('monthly_beta', mu = monthly_mu, sigma=monthly_sigma, shape = monthly_n_components*2, dims = 'locations')
monthly_seasonality = pm.Deterministic('monthly_seasonality',at.dot(monthly_X(t, 30.5/len(t)), monthly_beta), dims = 'locations')
predictions = pm.Deterministic('predictions', np.exp(trend[location_idx_] + yearly_seasonality[location_idx_] + monthly_seasonality[location_idx_]))
pm.Normal('obs',
mu = predictions,
sigma = error,
observed=df_sat['eaches'],
dims = 'obs_id'
)
trace_locations = pymc.sampling_jax.sample_numpyro_nuts(tune=2000, draws = 2000)
This is what coords_ looks like:
{'locations': Index(['kr_01', 'kr_02', 'kr_03', 'kr_04', 'kr_05', 'kr_06', 'kr_07', 'kr_08',
'kr_09', 'kr_41', 'kr_43', 'kr_46', 'kr_47', 'kr_48', 'kr_49', 'kr_80'],
dtype='object'),
'obs_id': array([ 0, 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, 645, 646, 647, 648, 649,
650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,
676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688,
689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701,
702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714,
715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727,
728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740,
741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753,
754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765])}
When I try to run the above, I get the following error:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_5309/847387806.py in <module>
14 growth = k + at.dot(a, delta)
15 offset = (m + at.dot(a, -s * delta))
---> 16 trend = pm.Deterministic('trend', growth[location_idx_] * t[location_idx_] + offset[location_idx_], dims = 'locations')
17
18 yearly_mu = pm.Normal('yearly_mu', 0, 1)
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
I look at the index ‘location_idx’ and it shows a dictionalry from {0...15} so I’m not sure why I’m getting an only integers.... error. Is there a mistake in the model code?