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lilianweng / stock-rnn

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
https://lilianweng.github.io/lil-log
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TypeError: range object does not support item assignment #8

Closed tarunparmar closed 6 years ago

tarunparmar commented 6 years ago

Hi,

I had to convert the code to run on Python3. Mostly changed all print statements, thats it.

For line 202 in model_rnn.py I changed the xrange to range. But could not get it to work. Tried looking up for a fix but its something beyond my understanding I guess so thought of opening up an issue here.

Any suggestions would be helpful. Thx.

(C:\GFApps\Anaconda3) C:\Users\tparmar\Documents\Python\predict stock market pri
ce using rnn>python main.py --stock_count=100 --train --input_size=1 --lstm_size
=128 --max_epoch=50 --embed_size=8
{'batch_size': 64,
 'embed_size': 8,
 'init_epoch': 5,
 'init_learning_rate': 0.001,
 'input_size': 1,
 'keep_prob': 0.8,
 'learning_rate_decay': 0.99,
 'lstm_size': 128,
 'max_epoch': 50,
 'num_layers': 1,
 'num_steps': 30,
 'sample_size': 4,
 'stock_count': 100,
 'stock_symbol': None,
 'train': True}
2017-12-16 23:32:55.276680: I C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\
36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instruct
ions that this TensorFlow binary was not compiled to use: AVX AVX2
C:\GFApps\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py:96
: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shap
e. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
---------
Variables: name (type shape) [size]
---------
embed_matrix:0 (float32_ref 100x8) [800, bytes: 3200]
dynamic_rnn/lstm_cell/kernel:0 (float32_ref 129x512) [66048, bytes: 264192]
dynamic_rnn/lstm_cell/bias:0 (float32_ref 512) [512, bytes: 2048]
w:0 (float32_ref 128x1) [128, bytes: 512]
b:0 (float32_ref 1) [1, bytes: 4]
Total size of variables: 67489
Total bytes of variables: 269956
{True: 497, False: 8}
main.py:58: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=
.....)
  info = info.sort('market_cap', ascending=False).reset_index(drop=True)
Head of S&P 500 info:
   symbol                  name                  sector   price  \
0   AAPL            Apple Inc.  Information Technology  139.52
1  GOOGL  Alphabet Inc Class A  Information Technology  851.15
2   GOOG  Alphabet Inc Class C  Information Technology  831.91
3   MSFT       Microsoft Corp.  Information Technology   64.40
4   AMZN        Amazon.com Inc  Consumer Discretionary  846.02

   dividend_yield  price/earnings  earnings/share  book_value  52_week_low  \
0            1.63           16.75            8.33       25.19        89.47
1             NaN           30.53           27.88      201.12       672.66
2             NaN           29.84           27.88      201.12       663.28
3            2.43           30.31            2.12        8.90        48.03
4             NaN          172.66            4.90       40.43       538.58

   52_week_high  market_cap  ebitda  price/sales  price/book  \
0        140.28      732.00   69.75         3.35        5.53
1        867.00      588.50   29.86         6.49        4.21
2        841.95      575.20   29.86         6.34        4.12
3         65.91      497.65   27.74         5.80        7.22
4        860.86      403.70   11.67         2.97       20.94

                                         sec_filings file_exists
0  http://www.sec.gov/cgi-bin/browse-edgar?action...        True
1  http://www.sec.gov/cgi-bin/browse-edgar?action...        True
2  http://www.sec.gov/cgi-bin/browse-edgar?action...        True
3  http://www.sec.gov/cgi-bin/browse-edgar?action...        True
4  http://www.sec.gov/cgi-bin/browse-edgar?action...        True
len(merged_test_X) = 17838
len(merged_test_y) = 17838
len(merged_test_labels) = 17838
{'AAPL': 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]), 'GOOGL': array([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]), 'GOOG': array([3
66, 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]), 'MSFT': array([5
33, 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])}
Start training for stocks: ['AAPL', 'GOOGL', 'GOOG', 'MSFT', 'AMZN', 'FB', 'XOM'
, 'JNJ', 'JPM', 'WFC', 'BAC', 'GE', 'T', 'PG', 'WMT', 'CVX', 'V', 'PFE', 'VZ', '
MRK', 'KO', 'CMCSA', 'HD', 'DIS', 'ORCL', 'PM', 'CSCO', 'IBM', 'INTC', 'C', 'UNH
', 'PEP', 'MO', 'AMGN', 'MA', 'MMM', 'MDT', 'BA', 'SLB', 'KHC', 'MCD', 'GS', 'AB
BV', 'HON', 'CELG', 'BMY', 'NKE', 'USB', 'WBA', 'UPS', 'UTX', 'GILD', 'UNP', 'AV
GO', 'RAI', 'LLY', 'CHTR', 'MS', 'CVS', 'PCLN', 'QCOM', 'SBUX', 'AGN', 'TXN', 'A
BT', 'ACN', 'DOW', 'TWX', 'COST', 'AXP', 'LOW', 'DD', 'MDLZ', 'CL', 'CB', 'BLK',
 'BIIB', 'AIG', 'PNC', 'TMO', 'NEE', 'NFLX', 'DHR', 'ADBE', 'COP', 'NVDA', 'CRM'
, 'MET', 'GD', 'EOG', 'DUK', 'FOXA', 'CAT', 'GM', 'FOX', 'SCHW', 'SPG', 'PYPL',
'TJX', 'FDX']
Traceback (most recent call last):
  File "main.py", line 112, in <module>
    tf.app.run()
  File "C:\GFApps\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py"
, line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "main.py", line 105, in main
    rnn_model.train(stock_data_list, FLAGS)
  File "C:\Users\tparmar\Documents\Python\predict stock market price using rnn\m
odel_rnn.py", line 209, in train
    for batch_X, batch_y in d_.generate_one_epoch(config.batch_size):
  File "C:\Users\tparmar\Documents\Python\predict stock market price using rnn\d
ata_model.py", line 65, in generate_one_epoch
    random.shuffle(batch_indices)
  File "C:\GFApps\Anaconda3\lib\random.py", line 274, in shuffle
    x[i], x[j] = x[j], x[i]
TypeError: 'range' object does not support item assignment

(C:\GFApps\Anaconda3) C:\Users\tparmar\Documents\Python\predict stock market pri
ce using rnn>
raagas commented 6 years ago

@tarunparmar: You need to change batch_indices = range(num_batches) into batch_indices = list(range(num_batches)).

tarunparmar commented 6 years ago

thanks @raagas , that did help plus a few other changes by converting xrange to range and iteritems to items.

sowmyakavali commented 3 years ago

@tarunparmar: You need to change batch_indices = range(num_batches) into batch_indices = list(range(num_batches)).

Here the problem is with file in root directory "/usr/lib/python3.6/random.py" How can I overcome this?

Traceback (most recent call last):

  File "train_c3d.py", line 342, in <module>
    tf.app.run()
  File "/home/apiiit-rkv/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/apiiit-rkv/.local/lib/python3.6/site-packages/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/apiiit-rkv/.local/lib/python3.6/site-packages/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "train_c3d.py", line 338, in main
    run_training()
  File "train_c3d.py", line 257, in run_training
    shuffle=True)
  File "/home/apiiit-rkv/Desktop/C3D-tensorflow/input_data.py", line 61, in read_clip_and_label
    random.shuffle(video_indices)
  File "/usr/lib/python3.6/random.py", line 277, in shuffle
    x[i], x[j] = x[j], x[i]
TypeError: 'range' object does not support item assignment