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Last active August 12, 2016 22:38
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polylearn linear solver bug
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fmbug.py
import numpy as np
import scipy.sparse as sp
from polylearn import FactorizationMachineClassifier
indices = np.array(
[2, 2, 2, 1, 2, 2, 36, 0, 35, 3, 2, 1, 3, 46, 2, 32, 2, 46, 2, 46, 3, 9, 3,
2, 46, 2, 2, 46, 3, 9, 0, 1, 45, 3, 3, 2, 3, 9, 3, 2, 10, 2, 3, 46, 3, 40,
2, 6, 3, 0, 42, 2, 3, 2, 46, 2, 46, 2, 46, 2, 0, 42, 2, 3, 46, 3, 2, 3,
46, 2, 46, 1, 9, 2, 0, 37, 2, 2, 3, 46, 2, 26, 3, 9, 2, 3, 46, 3, 46, 2,
9, 3, 9, 3, 7, 2, 2, 14, 1, 46, 3, 46, 3, 46, 3, 3, 9, 3, 46, 1, 46, 3, 2,
46, 2, 1, 9, 3, 46, 2, 9, 2, 31, 3, 9, 0, 23, 2, 10, 2, 2, 46, 2, 9, 3, 9,
2, 2, 16, 2, 31, 2, 2, 9, 2, 2, 2, 12, 3, 46, 2, 38, 0, 2, 21, 2, 46, 3,
2, 31, 2, 33, 3, 46, 2, 1, 46, 2, 46, 2, 3, 46, 2, 2, 46, 3, 9, 2, 2, 9,
2, 2, 3, 9, 2, 46, 3, 46, 2, 3, 3, 46, 1, 46, 3, 46, 3, 46, 3, 2, 47, 1,
3, 46, 2, 46, 3, 46, 2, 2, 46, 2, 2, 3, 2, 3, 46, 2, 2, 9, 3, 46, 3, 46,
3, 41, 3, 47, 2, 10, 2, 2, 36, 2, 46, 0, 0, 2, 2, 2, 9, 2, 2, 46, 3, 46,
2, 3, 47, 3, 46, 3, 46, 2, 2, 3, 46, 2, 25, 2, 2, 17, 2, 9, 2, 2, 2, 3,
46, 3, 46, 2, 2, 1, 46, 3, 46, 3, 9, 1, 9, 3, 1, 3, 46, 2, 2, 3, 46, 3,
46, 3, 9, 0, 42, 2, 46, 0, 10, 2, 3, 9, 0, 46, 3, 9, 3, 46, 2, 46, 2, 32,
2, 46, 3, 46, 3, 9, 2, 31, 2, 19, 2, 2, 46, 3, 46, 3, 46, 2, 46, 3, 46, 3,
22, 2, 1, 9, 2, 2, 3, 2, 2, 46, 2, 9, 2, 2, 2, 2, 3, 46, 3, 46, 1, 46, 3,
46, 3, 9, 2, 9, 3, 9, 2, 36, 3, 9, 2, 31, 2, 46, 1, 46, 3, 39, 2, 2, 2, 9,
2, 0, 15, 2, 32, 3, 46, 2, 3, 3, 46, 2, 2, 2, 3, 20, 3, 7, 3, 3, 9, 2, 1,
46, 3, 46, 2, 3, 46, 2, 2, 34, 3, 9, 2, 3, 46, 2, 2, 2, 9, 3, 46, 3, 2, 3,
3, 2, 46, 1, 9, 2, 46, 3, 46, 3, 46, 3, 46, 2, 3, 2, 2, 46, 2, 46, 3, 9,
3, 46, 3, 9, 3, 2, 13, 2, 2, 2, 2, 8, 2, 2, 43, 3, 3, 28, 2, 2, 46, 2, 46,
2, 24, 2, 46, 2, 3, 46, 3, 9, 3, 2, 46, 2, 3, 1, 9, 3, 46, 1, 46, 3, 46,
3, 46, 3, 9, 2, 1, 46, 3, 2, 3, 3, 46, 3, 3, 46, 2, 5, 2, 3, 3, 46, 3, 2,
3, 3, 46, 2, 3, 46, 2, 3, 9, 2, 34, 2, 2, 3, 9, 0, 46, 3, 9, 1, 1, 46, 0,
11, 2, 2, 0, 2, 1, 4, 2, 49, 1, 46, 1, 46, 2, 32, 1, 46, 3, 46, 3, 46, 2,
46, 2, 2, 2, 2, 6, 2, 46, 2, 2, 30, 3, 46, 3, 46, 2, 27, 1, 46, 3, 9, 3,
46, 3, 10, 2, 46, 3, 3, 46, 2, 46, 3, 46, 3, 2, 2, 32, 2, 46, 2, 2, 46, 2,
46, 3, 46, 2, 2, 0, 3, 2, 1, 46, 2, 2, 3, 9, 3, 3, 46, 2, 2, 18, 2, 46, 1,
3, 3, 2, 46, 3, 46, 1, 9, 2, 3, 46, 1, 46, 3, 2, 2, 3, 9, 2, 2, 3, 9, 3,
9, 2, 2, 44, 1, 3, 1, 9, 2, 3, 9, 2, 31, 2, 32, 2, 2, 48, 2, 2, 2, 46, 3,
46, 2, 2, 0, 2, 9, 2, 3, 3, 46, 2, 1, 46, 3, 46, 3, 2, 1, 46, 3, 9, 1, 46,
2, 9, 1, 46, 2, 31, 0, 29, 2, 2, 46, 1, 46, 2, 3, 0, 9, 2, 46, 2, 9, 3,
46])
indptr = np.array(
[0, 1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 14, 16, 18, 20, 22, 23, 25, 26, 28,
30, 31, 33, 34, 35, 36, 38, 39, 41, 42, 44, 46, 48, 49, 51, 52, 53, 55,
57, 59, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74, 76, 77, 78, 80, 82, 84,
85, 87, 89, 91, 93, 95, 96, 98, 100, 102, 104, 105, 107, 109, 111, 112,
114, 115, 117, 119, 121, 123, 125, 127, 129, 130, 132, 134, 136, 137, 139,
141, 142, 144, 145, 146, 148, 150, 152, 153, 155, 157, 158, 160, 162, 164,
165, 167, 169, 170, 172, 173, 175, 177, 178, 180, 181, 182, 184, 186, 188,
189, 190, 192, 194, 196, 198, 199, 201, 202, 204, 206, 208, 209, 211, 212,
213, 214, 215, 217, 218, 220, 222, 224, 226, 228, 230, 231, 233, 235, 236,
237, 238, 239, 241, 242, 244, 246, 247, 249, 251, 253, 254, 255, 257, 259,
260, 262, 264, 265, 266, 267, 269, 271, 272, 273, 275, 277, 279, 281, 282,
283, 285, 286, 287, 289, 291, 293, 295, 297, 299, 300, 302, 304, 306, 308,
310, 312, 314, 316, 318, 320, 322, 323, 325, 327, 329, 331, 333, 335, 336,
338, 339, 340, 341, 342, 344, 346, 347, 348, 349, 350, 352, 354, 356, 358,
360, 362, 364, 366, 368, 370, 372, 374, 376, 377, 378, 380, 381, 383, 385,
387, 388, 389, 391, 392, 393, 394, 396, 398, 399, 401, 402, 404, 406, 407,
409, 410, 412, 414, 415, 417, 418, 419, 421, 423, 424, 425, 426, 427, 429,
431, 433, 435, 437, 439, 440, 441, 442, 444, 446, 448, 450, 452, 453, 455,
456, 457, 458, 460, 461, 463, 464, 466, 467, 469, 471, 473, 475, 476, 478,
480, 481, 483, 484, 485, 487, 489, 491, 493, 495, 497, 498, 500, 501, 502,
503, 505, 506, 508, 510, 511, 512, 514, 515, 516, 517, 519, 520, 522, 523,
525, 527, 528, 529, 531, 533, 535, 536, 538, 540, 541, 542, 543, 544, 546,
548, 550, 552, 554, 556, 558, 560, 562, 563, 564, 565, 567, 569, 570, 572,
574, 576, 578, 580, 582, 584, 586, 588, 589, 591, 593, 595, 596, 597, 599,
601, 602, 604, 606, 608, 609, 610, 611, 612, 613, 615, 616, 617, 619, 620,
622, 623, 625, 627, 628, 629, 630, 632, 634, 636, 637, 639, 641, 642, 643,
644, 646, 647, 648, 650, 652, 653, 655, 656, 657, 659, 660, 662, 664, 666,
667, 669, 670, 671, 673, 675, 676, 677, 678, 680, 681, 682, 684, 685, 687,
689, 690, 691, 693, 695, 697, 699, 701, 703, 705, 706, 708, 710, 711, 712,
714, 716, 718, 720])
y = np.array(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,
0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
1])
X = sp.csr_matrix((np.ones_like(indices, dtype=np.double), indices, indptr))
if __name__ == '__main__':
fm = FactorizationMachineClassifier(n_components=1,
alpha=0.001,
beta=100,
fit_linear=True,
fit_lower=False,
degree=2,
init_lambdas='ones',
loss='squared',
max_iter=10,
verbose=True,
random_state=0)
fm.fit(X, y)
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