├── README.md ├── data ├── abalone.csv ├── features_lungcancer.csv ├── lungcancer.csv ├── oil.csv └── pima.csv └── src ├── fcbf.py └── utilities.py /README.md: -------------------------------------------------------------------------------- 1 | Fast Correlation-Based Filter (FCBF) selection. 2 | ==== 3 | 4 | Paper: http://www.public.asu.edu/~huanliu/papers/icml03.pdf 5 | 6 | Implementation of the FCBF algorithm. 7 | 8 | ## System requirements 9 | 10 | * Requires Python 2.7 11 | * Works on Linux and Mac. No guarantees for other OS, but may work. 12 | 13 | ## Usage 14 | 15 | Input file format: 16 | 17 | Row: observation vector, Col: Feature/Variable vector 18 | 19 | In Python, call 20 | 21 | `fcbf_wrapper(inpath, thresh, delim=',', header=False, classAt=-1)` 22 | 23 | OR 24 | 25 | From command line, 26 | 27 | > cd FCBF 28 | > python src/fcbf.py -h 29 | usage: fcbf.py [-h] [-inpath] [-thresh] [-delim] [-header] [-classAt] 30 | 31 | Fast Correlation-Based Filter Selection (FCBF) 32 | 33 | optional arguments: 34 | -h, --help show this help message and exit 35 | -inpath Path to input file 36 | -thresh SU threshold 37 | -delim File delimiter 38 | -header Contains header? 39 | -classAt Index of class column 40 | 41 | > python src/fcbf.py -inpath='./data/lungcancer.csv' -thresh=0.05 42 | Reading file. Please wait ... 43 | Success! Dimensions: 32 x 57 44 | Performing FCBF selection. Please wait ... 45 | Done! 46 | 47 | #Features selected: 6 48 | Selected feature indices: 49 | [[ 0.32054501 39. ] 50 | [ 0.32017586 19. ] 51 | [ 0.19562365 55. ] 52 | [ 0.15251083 1. ] 53 | [ 0.12478091 9. ] 54 | [ 0.07640196 2. ]] 55 | 56 | File saved successfully. Path: ../data/features_lungcancer.csv 57 | 58 | -------------------------------------------------------------------------------- /data/features_lungcancer.csv: -------------------------------------------------------------------------------- 1 | # SU, 0-based Feature 2 | 0.32054501,39 3 | 0.32017586,19 4 | 0.19562365,55 5 | 0.15251083,1 6 | 0.12478091,9 7 | 0.07640196,2 8 | -------------------------------------------------------------------------------- /data/lungcancer.csv: -------------------------------------------------------------------------------- 1 | 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13,106,70,0,0,34.2,0.251,52,0 276 | 2,100,70,52,57,40.5,0.677,25,0 277 | 7,106,60,24,0,26.5,0.296,29,1 278 | 0,104,64,23,116,27.8,0.454,23,0 279 | 5,114,74,0,0,24.9,0.744,57,0 280 | 2,108,62,10,278,25.3,0.881,22,0 281 | 0,146,70,0,0,37.9,0.334,28,1 282 | 10,129,76,28,122,35.9,0.28,39,0 283 | 7,133,88,15,155,32.4,0.262,37,0 284 | 7,161,86,0,0,30.4,0.165,47,1 285 | 2,108,80,0,0,27,0.259,52,1 286 | 7,136,74,26,135,26,0.647,51,0 287 | 5,155,84,44,545,38.7,0.619,34,0 288 | 1,119,86,39,220,45.6,0.808,29,1 289 | 4,96,56,17,49,20.8,0.34,26,0 290 | 5,108,72,43,75,36.1,0.263,33,0 291 | 0,78,88,29,40,36.9,0.434,21,0 292 | 0,107,62,30,74,36.6,0.757,25,1 293 | 2,128,78,37,182,43.3,1.224,31,1 294 | 1,128,48,45,194,40.5,0.613,24,1 295 | 0,161,50,0,0,21.9,0.254,65,0 296 | 6,151,62,31,120,35.5,0.692,28,0 297 | 2,146,70,38,360,28,0.337,29,1 298 | 0,126,84,29,215,30.7,0.52,24,0 299 | 14,100,78,25,184,36.6,0.412,46,1 300 | 8,112,72,0,0,23.6,0.84,58,0 301 | 0,167,0,0,0,32.3,0.839,30,1 302 | 2,144,58,33,135,31.6,0.422,25,1 303 | 5,77,82,41,42,35.8,0.156,35,0 304 | 5,115,98,0,0,52.9,0.209,28,1 305 | 3,150,76,0,0,21,0.207,37,0 306 | 2,120,76,37,105,39.7,0.215,29,0 307 | 10,161,68,23,132,25.5,0.326,47,1 308 | 0,137,68,14,148,24.8,0.143,21,0 309 | 0,128,68,19,180,30.5,1.391,25,1 310 | 2,124,68,28,205,32.9,0.875,30,1 311 | 6,80,66,30,0,26.2,0.313,41,0 312 | 0,106,70,37,148,39.4,0.605,22,0 313 | 2,155,74,17,96,26.6,0.433,27,1 314 | 3,113,50,10,85,29.5,0.626,25,0 315 | 7,109,80,31,0,35.9,1.127,43,1 316 | 2,112,68,22,94,34.1,0.315,26,0 317 | 3,99,80,11,64,19.3,0.284,30,0 318 | 3,182,74,0,0,30.5,0.345,29,1 319 | 3,115,66,39,140,38.1,0.15,28,0 320 | 6,194,78,0,0,23.5,0.129,59,1 321 | 4,129,60,12,231,27.5,0.527,31,0 322 | 3,112,74,30,0,31.6,0.197,25,1 323 | 0,124,70,20,0,27.4,0.254,36,1 324 | 13,152,90,33,29,26.8,0.731,43,1 325 | 2,112,75,32,0,35.7,0.148,21,0 326 | 1,157,72,21,168,25.6,0.123,24,0 327 | 1,122,64,32,156,35.1,0.692,30,1 328 | 10,179,70,0,0,35.1,0.2,37,0 329 | 2,102,86,36,120,45.5,0.127,23,1 330 | 6,105,70,32,68,30.8,0.122,37,0 331 | 8,118,72,19,0,23.1,1.476,46,0 332 | 2,87,58,16,52,32.7,0.166,25,0 333 | 1,180,0,0,0,43.3,0.282,41,1 334 | 12,106,80,0,0,23.6,0.137,44,0 335 | 1,95,60,18,58,23.9,0.26,22,0 336 | 0,165,76,43,255,47.9,0.259,26,0 337 | 0,117,0,0,0,33.8,0.932,44,0 338 | 5,115,76,0,0,31.2,0.343,44,1 339 | 9,152,78,34,171,34.2,0.893,33,1 340 | 7,178,84,0,0,39.9,0.331,41,1 341 | 1,130,70,13,105,25.9,0.472,22,0 342 | 1,95,74,21,73,25.9,0.673,36,0 343 | 1,0,68,35,0,32,0.389,22,0 344 | 5,122,86,0,0,34.7,0.29,33,0 345 | 8,95,72,0,0,36.8,0.485,57,0 346 | 8,126,88,36,108,38.5,0.349,49,0 347 | 1,139,46,19,83,28.7,0.654,22,0 348 | 3,116,0,0,0,23.5,0.187,23,0 349 | 3,99,62,19,74,21.8,0.279,26,0 350 | 5,0,80,32,0,41,0.346,37,1 351 | 4,92,80,0,0,42.2,0.237,29,0 352 | 4,137,84,0,0,31.2,0.252,30,0 353 | 3,61,82,28,0,34.4,0.243,46,0 354 | 1,90,62,12,43,27.2,0.58,24,0 355 | 3,90,78,0,0,42.7,0.559,21,0 356 | 9,165,88,0,0,30.4,0.302,49,1 357 | 1,125,50,40,167,33.3,0.962,28,1 358 | 13,129,0,30,0,39.9,0.569,44,1 359 | 12,88,74,40,54,35.3,0.378,48,0 360 | 1,196,76,36,249,36.5,0.875,29,1 361 | 5,189,64,33,325,31.2,0.583,29,1 362 | 5,158,70,0,0,29.8,0.207,63,0 363 | 5,103,108,37,0,39.2,0.305,65,0 364 | 4,146,78,0,0,38.5,0.52,67,1 365 | 4,147,74,25,293,34.9,0.385,30,0 366 | 5,99,54,28,83,34,0.499,30,0 367 | 6,124,72,0,0,27.6,0.368,29,1 368 | 0,101,64,17,0,21,0.252,21,0 369 | 3,81,86,16,66,27.5,0.306,22,0 370 | 1,133,102,28,140,32.8,0.234,45,1 371 | 3,173,82,48,465,38.4,2.137,25,1 372 | 0,118,64,23,89,0,1.731,21,0 373 | 0,84,64,22,66,35.8,0.545,21,0 374 | 2,105,58,40,94,34.9,0.225,25,0 375 | 2,122,52,43,158,36.2,0.816,28,0 376 | 12,140,82,43,325,39.2,0.528,58,1 377 | 0,98,82,15,84,25.2,0.299,22,0 378 | 1,87,60,37,75,37.2,0.509,22,0 379 | 4,156,75,0,0,48.3,0.238,32,1 380 | 0,93,100,39,72,43.4,1.021,35,0 381 | 1,107,72,30,82,30.8,0.821,24,0 382 | 0,105,68,22,0,20,0.236,22,0 383 | 1,109,60,8,182,25.4,0.947,21,0 384 | 1,90,62,18,59,25.1,1.268,25,0 385 | 1,125,70,24,110,24.3,0.221,25,0 386 | 1,119,54,13,50,22.3,0.205,24,0 387 | 5,116,74,29,0,32.3,0.66,35,1 388 | 8,105,100,36,0,43.3,0.239,45,1 389 | 5,144,82,26,285,32,0.452,58,1 390 | 3,100,68,23,81,31.6,0.949,28,0 391 | 1,100,66,29,196,32,0.444,42,0 392 | 5,166,76,0,0,45.7,0.34,27,1 393 | 1,131,64,14,415,23.7,0.389,21,0 394 | 4,116,72,12,87,22.1,0.463,37,0 395 | 4,158,78,0,0,32.9,0.803,31,1 396 | 2,127,58,24,275,27.7,1.6,25,0 397 | 3,96,56,34,115,24.7,0.944,39,0 398 | 0,131,66,40,0,34.3,0.196,22,1 399 | 3,82,70,0,0,21.1,0.389,25,0 400 | 3,193,70,31,0,34.9,0.241,25,1 401 | 4,95,64,0,0,32,0.161,31,1 402 | 6,137,61,0,0,24.2,0.151,55,0 403 | 5,136,84,41,88,35,0.286,35,1 404 | 9,72,78,25,0,31.6,0.28,38,0 405 | 5,168,64,0,0,32.9,0.135,41,1 406 | 2,123,48,32,165,42.1,0.52,26,0 407 | 4,115,72,0,0,28.9,0.376,46,1 408 | 0,101,62,0,0,21.9,0.336,25,0 409 | 8,197,74,0,0,25.9,1.191,39,1 410 | 1,172,68,49,579,42.4,0.702,28,1 411 | 6,102,90,39,0,35.7,0.674,28,0 412 | 1,112,72,30,176,34.4,0.528,25,0 413 | 1,143,84,23,310,42.4,1.076,22,0 414 | 1,143,74,22,61,26.2,0.256,21,0 415 | 0,138,60,35,167,34.6,0.534,21,1 416 | 3,173,84,33,474,35.7,0.258,22,1 417 | 1,97,68,21,0,27.2,1.095,22,0 418 | 4,144,82,32,0,38.5,0.554,37,1 419 | 1,83,68,0,0,18.2,0.624,27,0 420 | 3,129,64,29,115,26.4,0.219,28,1 421 | 1,119,88,41,170,45.3,0.507,26,0 422 | 2,94,68,18,76,26,0.561,21,0 423 | 0,102,64,46,78,40.6,0.496,21,0 424 | 2,115,64,22,0,30.8,0.421,21,0 425 | 8,151,78,32,210,42.9,0.516,36,1 426 | 4,184,78,39,277,37,0.264,31,1 427 | 0,94,0,0,0,0,0.256,25,0 428 | 1,181,64,30,180,34.1,0.328,38,1 429 | 0,135,94,46,145,40.6,0.284,26,0 430 | 1,95,82,25,180,35,0.233,43,1 431 | 2,99,0,0,0,22.2,0.108,23,0 432 | 3,89,74,16,85,30.4,0.551,38,0 433 | 1,80,74,11,60,30,0.527,22,0 434 | 2,139,75,0,0,25.6,0.167,29,0 435 | 1,90,68,8,0,24.5,1.138,36,0 436 | 0,141,0,0,0,42.4,0.205,29,1 437 | 12,140,85,33,0,37.4,0.244,41,0 438 | 5,147,75,0,0,29.9,0.434,28,0 439 | 1,97,70,15,0,18.2,0.147,21,0 440 | 6,107,88,0,0,36.8,0.727,31,0 441 | 0,189,104,25,0,34.3,0.435,41,1 442 | 2,83,66,23,50,32.2,0.497,22,0 443 | 4,117,64,27,120,33.2,0.23,24,0 444 | 8,108,70,0,0,30.5,0.955,33,1 445 | 4,117,62,12,0,29.7,0.38,30,1 446 | 0,180,78,63,14,59.4,2.42,25,1 447 | 1,100,72,12,70,25.3,0.658,28,0 448 | 0,95,80,45,92,36.5,0.33,26,0 449 | 0,104,64,37,64,33.6,0.51,22,1 450 | 0,120,74,18,63,30.5,0.285,26,0 451 | 1,82,64,13,95,21.2,0.415,23,0 452 | 2,134,70,0,0,28.9,0.542,23,1 453 | 0,91,68,32,210,39.9,0.381,25,0 454 | 2,119,0,0,0,19.6,0.832,72,0 455 | 2,100,54,28,105,37.8,0.498,24,0 456 | 14,175,62,30,0,33.6,0.212,38,1 457 | 1,135,54,0,0,26.7,0.687,62,0 458 | 5,86,68,28,71,30.2,0.364,24,0 459 | 10,148,84,48,237,37.6,1.001,51,1 460 | 9,134,74,33,60,25.9,0.46,81,0 461 | 9,120,72,22,56,20.8,0.733,48,0 462 | 1,71,62,0,0,21.8,0.416,26,0 463 | 8,74,70,40,49,35.3,0.705,39,0 464 | 5,88,78,30,0,27.6,0.258,37,0 465 | 10,115,98,0,0,24,1.022,34,0 466 | 0,124,56,13,105,21.8,0.452,21,0 467 | 0,74,52,10,36,27.8,0.269,22,0 468 | 0,97,64,36,100,36.8,0.6,25,0 469 | 8,120,0,0,0,30,0.183,38,1 470 | 6,154,78,41,140,46.1,0.571,27,0 471 | 1,144,82,40,0,41.3,0.607,28,0 472 | 0,137,70,38,0,33.2,0.17,22,0 473 | 0,119,66,27,0,38.8,0.259,22,0 474 | 7,136,90,0,0,29.9,0.21,50,0 475 | 4,114,64,0,0,28.9,0.126,24,0 476 | 0,137,84,27,0,27.3,0.231,59,0 477 | 2,105,80,45,191,33.7,0.711,29,1 478 | 7,114,76,17,110,23.8,0.466,31,0 479 | 8,126,74,38,75,25.9,0.162,39,0 480 | 4,132,86,31,0,28,0.419,63,0 481 | 3,158,70,30,328,35.5,0.344,35,1 482 | 0,123,88,37,0,35.2,0.197,29,0 483 | 4,85,58,22,49,27.8,0.306,28,0 484 | 0,84,82,31,125,38.2,0.233,23,0 485 | 0,145,0,0,0,44.2,0.63,31,1 486 | 0,135,68,42,250,42.3,0.365,24,1 487 | 1,139,62,41,480,40.7,0.536,21,0 488 | 0,173,78,32,265,46.5,1.159,58,0 489 | 4,99,72,17,0,25.6,0.294,28,0 490 | 8,194,80,0,0,26.1,0.551,67,0 491 | 2,83,65,28,66,36.8,0.629,24,0 492 | 2,89,90,30,0,33.5,0.292,42,0 493 | 4,99,68,38,0,32.8,0.145,33,0 494 | 4,125,70,18,122,28.9,1.144,45,1 495 | 3,80,0,0,0,0,0.174,22,0 496 | 6,166,74,0,0,26.6,0.304,66,0 497 | 5,110,68,0,0,26,0.292,30,0 498 | 2,81,72,15,76,30.1,0.547,25,0 499 | 7,195,70,33,145,25.1,0.163,55,1 500 | 6,154,74,32,193,29.3,0.839,39,0 501 | 2,117,90,19,71,25.2,0.313,21,0 502 | 3,84,72,32,0,37.2,0.267,28,0 503 | 6,0,68,41,0,39,0.727,41,1 504 | 7,94,64,25,79,33.3,0.738,41,0 505 | 3,96,78,39,0,37.3,0.238,40,0 506 | 10,75,82,0,0,33.3,0.263,38,0 507 | 0,180,90,26,90,36.5,0.314,35,1 508 | 1,130,60,23,170,28.6,0.692,21,0 509 | 2,84,50,23,76,30.4,0.968,21,0 510 | 8,120,78,0,0,25,0.409,64,0 511 | 12,84,72,31,0,29.7,0.297,46,1 512 | 0,139,62,17,210,22.1,0.207,21,0 513 | 9,91,68,0,0,24.2,0.2,58,0 514 | 2,91,62,0,0,27.3,0.525,22,0 515 | 3,99,54,19,86,25.6,0.154,24,0 516 | 3,163,70,18,105,31.6,0.268,28,1 517 | 9,145,88,34,165,30.3,0.771,53,1 518 | 7,125,86,0,0,37.6,0.304,51,0 519 | 13,76,60,0,0,32.8,0.18,41,0 520 | 6,129,90,7,326,19.6,0.582,60,0 521 | 2,68,70,32,66,25,0.187,25,0 522 | 3,124,80,33,130,33.2,0.305,26,0 523 | 6,114,0,0,0,0,0.189,26,0 524 | 9,130,70,0,0,34.2,0.652,45,1 525 | 3,125,58,0,0,31.6,0.151,24,0 526 | 3,87,60,18,0,21.8,0.444,21,0 527 | 1,97,64,19,82,18.2,0.299,21,0 528 | 3,116,74,15,105,26.3,0.107,24,0 529 | 0,117,66,31,188,30.8,0.493,22,0 530 | 0,111,65,0,0,24.6,0.66,31,0 531 | 2,122,60,18,106,29.8,0.717,22,0 532 | 0,107,76,0,0,45.3,0.686,24,0 533 | 1,86,66,52,65,41.3,0.917,29,0 534 | 6,91,0,0,0,29.8,0.501,31,0 535 | 1,77,56,30,56,33.3,1.251,24,0 536 | 4,132,0,0,0,32.9,0.302,23,1 537 | 0,105,90,0,0,29.6,0.197,46,0 538 | 0,57,60,0,0,21.7,0.735,67,0 539 | 0,127,80,37,210,36.3,0.804,23,0 540 | 3,129,92,49,155,36.4,0.968,32,1 541 | 8,100,74,40,215,39.4,0.661,43,1 542 | 3,128,72,25,190,32.4,0.549,27,1 543 | 10,90,85,32,0,34.9,0.825,56,1 544 | 4,84,90,23,56,39.5,0.159,25,0 545 | 1,88,78,29,76,32,0.365,29,0 546 | 8,186,90,35,225,34.5,0.423,37,1 547 | 5,187,76,27,207,43.6,1.034,53,1 548 | 4,131,68,21,166,33.1,0.16,28,0 549 | 1,164,82,43,67,32.8,0.341,50,0 550 | 4,189,110,31,0,28.5,0.68,37,0 551 | 1,116,70,28,0,27.4,0.204,21,0 552 | 3,84,68,30,106,31.9,0.591,25,0 553 | 6,114,88,0,0,27.8,0.247,66,0 554 | 1,88,62,24,44,29.9,0.422,23,0 555 | 1,84,64,23,115,36.9,0.471,28,0 556 | 7,124,70,33,215,25.5,0.161,37,0 557 | 1,97,70,40,0,38.1,0.218,30,0 558 | 8,110,76,0,0,27.8,0.237,58,0 559 | 11,103,68,40,0,46.2,0.126,42,0 560 | 11,85,74,0,0,30.1,0.3,35,0 561 | 6,125,76,0,0,33.8,0.121,54,1 562 | 0,198,66,32,274,41.3,0.502,28,1 563 | 1,87,68,34,77,37.6,0.401,24,0 564 | 6,99,60,19,54,26.9,0.497,32,0 565 | 0,91,80,0,0,32.4,0.601,27,0 566 | 2,95,54,14,88,26.1,0.748,22,0 567 | 1,99,72,30,18,38.6,0.412,21,0 568 | 6,92,62,32,126,32,0.085,46,0 569 | 4,154,72,29,126,31.3,0.338,37,0 570 | 0,121,66,30,165,34.3,0.203,33,1 571 | 3,78,70,0,0,32.5,0.27,39,0 572 | 2,130,96,0,0,22.6,0.268,21,0 573 | 3,111,58,31,44,29.5,0.43,22,0 574 | 2,98,60,17,120,34.7,0.198,22,0 575 | 1,143,86,30,330,30.1,0.892,23,0 576 | 1,119,44,47,63,35.5,0.28,25,0 577 | 6,108,44,20,130,24,0.813,35,0 578 | 2,118,80,0,0,42.9,0.693,21,1 579 | 10,133,68,0,0,27,0.245,36,0 580 | 2,197,70,99,0,34.7,0.575,62,1 581 | 0,151,90,46,0,42.1,0.371,21,1 582 | 6,109,60,27,0,25,0.206,27,0 583 | 12,121,78,17,0,26.5,0.259,62,0 584 | 8,100,76,0,0,38.7,0.19,42,0 585 | 8,124,76,24,600,28.7,0.687,52,1 586 | 1,93,56,11,0,22.5,0.417,22,0 587 | 8,143,66,0,0,34.9,0.129,41,1 588 | 6,103,66,0,0,24.3,0.249,29,0 589 | 3,176,86,27,156,33.3,1.154,52,1 590 | 0,73,0,0,0,21.1,0.342,25,0 591 | 11,111,84,40,0,46.8,0.925,45,1 592 | 2,112,78,50,140,39.4,0.175,24,0 593 | 3,132,80,0,0,34.4,0.402,44,1 594 | 2,82,52,22,115,28.5,1.699,25,0 595 | 6,123,72,45,230,33.6,0.733,34,0 596 | 0,188,82,14,185,32,0.682,22,1 597 | 0,67,76,0,0,45.3,0.194,46,0 598 | 1,89,24,19,25,27.8,0.559,21,0 599 | 1,173,74,0,0,36.8,0.088,38,1 600 | 1,109,38,18,120,23.1,0.407,26,0 601 | 1,108,88,19,0,27.1,0.4,24,0 602 | 6,96,0,0,0,23.7,0.19,28,0 603 | 1,124,74,36,0,27.8,0.1,30,0 604 | 7,150,78,29,126,35.2,0.692,54,1 605 | 4,183,0,0,0,28.4,0.212,36,1 606 | 1,124,60,32,0,35.8,0.514,21,0 607 | 1,181,78,42,293,40,1.258,22,1 608 | 1,92,62,25,41,19.5,0.482,25,0 609 | 0,152,82,39,272,41.5,0.27,27,0 610 | 1,111,62,13,182,24,0.138,23,0 611 | 3,106,54,21,158,30.9,0.292,24,0 612 | 3,174,58,22,194,32.9,0.593,36,1 613 | 7,168,88,42,321,38.2,0.787,40,1 614 | 6,105,80,28,0,32.5,0.878,26,0 615 | 11,138,74,26,144,36.1,0.557,50,1 616 | 3,106,72,0,0,25.8,0.207,27,0 617 | 6,117,96,0,0,28.7,0.157,30,0 618 | 2,68,62,13,15,20.1,0.257,23,0 619 | 9,112,82,24,0,28.2,1.282,50,1 620 | 0,119,0,0,0,32.4,0.141,24,1 621 | 2,112,86,42,160,38.4,0.246,28,0 622 | 2,92,76,20,0,24.2,1.698,28,0 623 | 6,183,94,0,0,40.8,1.461,45,0 624 | 0,94,70,27,115,43.5,0.347,21,0 625 | 2,108,64,0,0,30.8,0.158,21,0 626 | 4,90,88,47,54,37.7,0.362,29,0 627 | 0,125,68,0,0,24.7,0.206,21,0 628 | 0,132,78,0,0,32.4,0.393,21,0 629 | 5,128,80,0,0,34.6,0.144,45,0 630 | 4,94,65,22,0,24.7,0.148,21,0 631 | 7,114,64,0,0,27.4,0.732,34,1 632 | 0,102,78,40,90,34.5,0.238,24,0 633 | 2,111,60,0,0,26.2,0.343,23,0 634 | 1,128,82,17,183,27.5,0.115,22,0 635 | 10,92,62,0,0,25.9,0.167,31,0 636 | 13,104,72,0,0,31.2,0.465,38,1 637 | 5,104,74,0,0,28.8,0.153,48,0 638 | 2,94,76,18,66,31.6,0.649,23,0 639 | 7,97,76,32,91,40.9,0.871,32,1 640 | 1,100,74,12,46,19.5,0.149,28,0 641 | 0,102,86,17,105,29.3,0.695,27,0 642 | 4,128,70,0,0,34.3,0.303,24,0 643 | 6,147,80,0,0,29.5,0.178,50,1 644 | 4,90,0,0,0,28,0.61,31,0 645 | 3,103,72,30,152,27.6,0.73,27,0 646 | 2,157,74,35,440,39.4,0.134,30,0 647 | 1,167,74,17,144,23.4,0.447,33,1 648 | 0,179,50,36,159,37.8,0.455,22,1 649 | 11,136,84,35,130,28.3,0.26,42,1 650 | 0,107,60,25,0,26.4,0.133,23,0 651 | 1,91,54,25,100,25.2,0.234,23,0 652 | 1,117,60,23,106,33.8,0.466,27,0 653 | 5,123,74,40,77,34.1,0.269,28,0 654 | 2,120,54,0,0,26.8,0.455,27,0 655 | 1,106,70,28,135,34.2,0.142,22,0 656 | 2,155,52,27,540,38.7,0.24,25,1 657 | 2,101,58,35,90,21.8,0.155,22,0 658 | 1,120,80,48,200,38.9,1.162,41,0 659 | 11,127,106,0,0,39,0.19,51,0 660 | 3,80,82,31,70,34.2,1.292,27,1 661 | 10,162,84,0,0,27.7,0.182,54,0 662 | 1,199,76,43,0,42.9,1.394,22,1 663 | 8,167,106,46,231,37.6,0.165,43,1 664 | 9,145,80,46,130,37.9,0.637,40,1 665 | 6,115,60,39,0,33.7,0.245,40,1 666 | 1,112,80,45,132,34.8,0.217,24,0 667 | 4,145,82,18,0,32.5,0.235,70,1 668 | 10,111,70,27,0,27.5,0.141,40,1 669 | 6,98,58,33,190,34,0.43,43,0 670 | 9,154,78,30,100,30.9,0.164,45,0 671 | 6,165,68,26,168,33.6,0.631,49,0 672 | 1,99,58,10,0,25.4,0.551,21,0 673 | 10,68,106,23,49,35.5,0.285,47,0 674 | 3,123,100,35,240,57.3,0.88,22,0 675 | 8,91,82,0,0,35.6,0.587,68,0 676 | 6,195,70,0,0,30.9,0.328,31,1 677 | 9,156,86,0,0,24.8,0.23,53,1 678 | 0,93,60,0,0,35.3,0.263,25,0 679 | 3,121,52,0,0,36,0.127,25,1 680 | 2,101,58,17,265,24.2,0.614,23,0 681 | 2,56,56,28,45,24.2,0.332,22,0 682 | 0,162,76,36,0,49.6,0.364,26,1 683 | 0,95,64,39,105,44.6,0.366,22,0 684 | 4,125,80,0,0,32.3,0.536,27,1 685 | 5,136,82,0,0,0,0.64,69,0 686 | 2,129,74,26,205,33.2,0.591,25,0 687 | 3,130,64,0,0,23.1,0.314,22,0 688 | 1,107,50,19,0,28.3,0.181,29,0 689 | 1,140,74,26,180,24.1,0.828,23,0 690 | 1,144,82,46,180,46.1,0.335,46,1 691 | 8,107,80,0,0,24.6,0.856,34,0 692 | 13,158,114,0,0,42.3,0.257,44,1 693 | 2,121,70,32,95,39.1,0.886,23,0 694 | 7,129,68,49,125,38.5,0.439,43,1 695 | 2,90,60,0,0,23.5,0.191,25,0 696 | 7,142,90,24,480,30.4,0.128,43,1 697 | 3,169,74,19,125,29.9,0.268,31,1 698 | 0,99,0,0,0,25,0.253,22,0 699 | 4,127,88,11,155,34.5,0.598,28,0 700 | 4,118,70,0,0,44.5,0.904,26,0 701 | 2,122,76,27,200,35.9,0.483,26,0 702 | 6,125,78,31,0,27.6,0.565,49,1 703 | 1,168,88,29,0,35,0.905,52,1 704 | 2,129,0,0,0,38.5,0.304,41,0 705 | 4,110,76,20,100,28.4,0.118,27,0 706 | 6,80,80,36,0,39.8,0.177,28,0 707 | 10,115,0,0,0,0,0.261,30,1 708 | 2,127,46,21,335,34.4,0.176,22,0 709 | 9,164,78,0,0,32.8,0.148,45,1 710 | 2,93,64,32,160,38,0.674,23,1 711 | 3,158,64,13,387,31.2,0.295,24,0 712 | 5,126,78,27,22,29.6,0.439,40,0 713 | 10,129,62,36,0,41.2,0.441,38,1 714 | 0,134,58,20,291,26.4,0.352,21,0 715 | 3,102,74,0,0,29.5,0.121,32,0 716 | 7,187,50,33,392,33.9,0.826,34,1 717 | 3,173,78,39,185,33.8,0.97,31,1 718 | 10,94,72,18,0,23.1,0.595,56,0 719 | 1,108,60,46,178,35.5,0.415,24,0 720 | 5,97,76,27,0,35.6,0.378,52,1 721 | 4,83,86,19,0,29.3,0.317,34,0 722 | 1,114,66,36,200,38.1,0.289,21,0 723 | 1,149,68,29,127,29.3,0.349,42,1 724 | 5,117,86,30,105,39.1,0.251,42,0 725 | 1,111,94,0,0,32.8,0.265,45,0 726 | 4,112,78,40,0,39.4,0.236,38,0 727 | 1,116,78,29,180,36.1,0.496,25,0 728 | 0,141,84,26,0,32.4,0.433,22,0 729 | 2,175,88,0,0,22.9,0.326,22,0 730 | 2,92,52,0,0,30.1,0.141,22,0 731 | 3,130,78,23,79,28.4,0.323,34,1 732 | 8,120,86,0,0,28.4,0.259,22,1 733 | 2,174,88,37,120,44.5,0.646,24,1 734 | 2,106,56,27,165,29,0.426,22,0 735 | 2,105,75,0,0,23.3,0.56,53,0 736 | 4,95,60,32,0,35.4,0.284,28,0 737 | 0,126,86,27,120,27.4,0.515,21,0 738 | 8,65,72,23,0,32,0.6,42,0 739 | 2,99,60,17,160,36.6,0.453,21,0 740 | 1,102,74,0,0,39.5,0.293,42,1 741 | 11,120,80,37,150,42.3,0.785,48,1 742 | 3,102,44,20,94,30.8,0.4,26,0 743 | 1,109,58,18,116,28.5,0.219,22,0 744 | 9,140,94,0,0,32.7,0.734,45,1 745 | 13,153,88,37,140,40.6,1.174,39,0 746 | 12,100,84,33,105,30,0.488,46,0 747 | 1,147,94,41,0,49.3,0.358,27,1 748 | 1,81,74,41,57,46.3,1.096,32,0 749 | 3,187,70,22,200,36.4,0.408,36,1 750 | 6,162,62,0,0,24.3,0.178,50,1 751 | 4,136,70,0,0,31.2,1.182,22,1 752 | 1,121,78,39,74,39,0.261,28,0 753 | 3,108,62,24,0,26,0.223,25,0 754 | 0,181,88,44,510,43.3,0.222,26,1 755 | 8,154,78,32,0,32.4,0.443,45,1 756 | 1,128,88,39,110,36.5,1.057,37,1 757 | 7,137,90,41,0,32,0.391,39,0 758 | 0,123,72,0,0,36.3,0.258,52,1 759 | 1,106,76,0,0,37.5,0.197,26,0 760 | 6,190,92,0,0,35.5,0.278,66,1 761 | 2,88,58,26,16,28.4,0.766,22,0 762 | 9,170,74,31,0,44,0.403,43,1 763 | 9,89,62,0,0,22.5,0.142,33,0 764 | 10,101,76,48,180,32.9,0.171,63,0 765 | 2,122,70,27,0,36.8,0.34,27,0 766 | 5,121,72,23,112,26.2,0.245,30,0 767 | 1,126,60,0,0,30.1,0.349,47,1 768 | 1,93,70,31,0,30.4,0.315,23,0 769 | -------------------------------------------------------------------------------- /src/fcbf.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # encoding: utf-8 3 | """ 4 | fcbf.py 5 | 6 | Created by Prashant Shiralkar on 2015-02-06. 7 | 8 | Fast Correlation-Based Filter (FCBF) algorithm as described in 9 | Feature Selection for High-Dimensional Data: A Fast Correlation-Based 10 | Filter Solution. Yu & Liu (ICML 2003) 11 | 12 | """ 13 | 14 | import sys 15 | import os 16 | import argparse 17 | import numpy as np 18 | 19 | def entropy(vec, base=2): 20 | " Returns the empirical entropy H(X) in the input vector." 21 | _, vec = np.unique(vec, return_counts=True) 22 | prob_vec = np.array(vec/float(sum(vec))) 23 | if base == 2: 24 | logfn = np.log2 25 | elif base == 10: 26 | logfn = np.log10 27 | else: 28 | logfn = np.log 29 | return prob_vec.dot(-logfn(prob_vec)) 30 | 31 | def conditional_entropy(x, y): 32 | "Returns H(X|Y)." 33 | uy, uyc = np.unique(y, return_counts=True) 34 | prob_uyc = uyc/float(sum(uyc)) 35 | cond_entropy_x = np.array([entropy(x[y == v]) for v in uy]) 36 | return prob_uyc.dot(cond_entropy_x) 37 | 38 | def mutual_information(x, y): 39 | " Returns the information gain/mutual information [H(X)-H(X|Y)] between two random vars x & y." 40 | return entropy(x) - conditional_entropy(x, y) 41 | 42 | def symmetrical_uncertainty(x, y): 43 | " Returns 'symmetrical uncertainty' (SU) - a symmetric mutual information measure." 44 | return 2.0*mutual_information(x, y)/(entropy(x) + entropy(y)) 45 | 46 | def getFirstElement(d): 47 | """ 48 | Returns tuple corresponding to first 'unconsidered' feature 49 | 50 | Parameters: 51 | ---------- 52 | d : ndarray 53 | A 2-d array with SU, original feature index and flag as columns. 54 | 55 | Returns: 56 | ------- 57 | a, b, c : tuple 58 | a - SU value, b - original feature index, c - index of next 'unconsidered' feature 59 | """ 60 | 61 | t = np.where(d[:,2]>0)[0] 62 | if len(t): 63 | return d[t[0],0], d[t[0],1], t[0] 64 | return None, None, None 65 | 66 | def getNextElement(d, idx): 67 | """ 68 | Returns tuple corresponding to the next 'unconsidered' feature. 69 | 70 | Parameters: 71 | ----------- 72 | d : ndarray 73 | A 2-d array with SU, original feature index and flag as columns. 74 | idx : int 75 | Represents original index of a feature whose next element is required. 76 | 77 | Returns: 78 | -------- 79 | a, b, c : tuple 80 | a - SU value, b - original feature index, c - index of next 'unconsidered' feature 81 | """ 82 | t = np.where(d[:,2]>0)[0] 83 | t = t[t > idx] 84 | if len(t): 85 | return d[t[0],0], d[t[0],1], t[0] 86 | return None, None, None 87 | 88 | def removeElement(d, idx): 89 | """ 90 | Returns data with requested feature removed. 91 | 92 | Parameters: 93 | ----------- 94 | d : ndarray 95 | A 2-d array with SU, original feature index and flag as columns. 96 | idx : int 97 | Represents original index of a feature which needs to be removed. 98 | 99 | Returns: 100 | -------- 101 | d : ndarray 102 | Same as input, except with specific feature removed. 103 | """ 104 | d[idx,2] = 0 105 | return d 106 | 107 | def c_correlation(X, y): 108 | """ 109 | Returns SU values between each feature and class. 110 | 111 | Parameters: 112 | ----------- 113 | X : 2-D ndarray 114 | Feature matrix. 115 | y : ndarray 116 | Class label vector 117 | 118 | Returns: 119 | -------- 120 | su : ndarray 121 | Symmetric Uncertainty (SU) values for each feature. 122 | """ 123 | su = np.zeros(X.shape[1]) 124 | for i in np.arange(X.shape[1]): 125 | su[i] = symmetrical_uncertainty(X[:,i], y) 126 | return su 127 | 128 | def fcbf(X, y, thresh): 129 | """ 130 | Perform Fast Correlation-Based Filter solution (FCBF). 131 | 132 | Parameters: 133 | ----------- 134 | X : 2-D ndarray 135 | Feature matrix 136 | y : ndarray 137 | Class label vector 138 | thresh : float 139 | A value in [0,1) used as threshold for selecting 'relevant' features. 140 | A negative value suggest the use of minimum SU[i,c] value as threshold. 141 | 142 | Returns: 143 | -------- 144 | sbest : 2-D ndarray 145 | An array containing SU[i,c] values and feature index i. 146 | """ 147 | n = X.shape[1] 148 | slist = np.zeros((n, 3)) 149 | slist[:, -1] = 1 150 | 151 | # identify relevant features 152 | slist[:,0] = c_correlation(X, y) # compute 'C-correlation' 153 | idx = slist[:,0].argsort()[::-1] 154 | slist = slist[idx, ] 155 | slist[:,1] = idx 156 | if thresh < 0: 157 | thresh = np.median(slist[-1,0]) 158 | print "Using minimum SU value as default threshold: {0}".format(thresh) 159 | elif thresh >= 1 or thresh > max(slist[:,0]): 160 | print "No relevant features selected for given threshold." 161 | print "Please lower the threshold and try again." 162 | exit() 163 | 164 | slist = slist[slist[:,0]>thresh,:] # desc. ordered per SU[i,c] 165 | 166 | # identify redundant features among the relevant ones 167 | cache = {} 168 | m = len(slist) 169 | p_su, p, p_idx = getFirstElement(slist) 170 | for i in xrange(m): 171 | p = int(p) 172 | q_su, q, q_idx = getNextElement(slist, p_idx) 173 | if q: 174 | while q: 175 | q = int(q) 176 | if (p, q) in cache: 177 | pq_su = cache[(p,q)] 178 | else: 179 | pq_su = symmetrical_uncertainty(X[:,p], X[:,q]) 180 | cache[(p,q)] = pq_su 181 | 182 | if pq_su >= q_su: 183 | slist = removeElement(slist, q_idx) 184 | q_su, q, q_idx = getNextElement(slist, q_idx) 185 | 186 | p_su, p, p_idx = getNextElement(slist, p_idx) 187 | if not p_idx: 188 | break 189 | 190 | sbest = slist[slist[:,2]>0, :2] 191 | return sbest 192 | 193 | def fcbf_wrapper(inpath, thresh, delim=',', header=False, classAt=-1): 194 | """ 195 | Main function call to perform FCBF selection. Saves Symmetric Uncertainty (SU) 196 | values and 0-based indices of selected features to a CSV file at the same location 197 | as input file, with 'feature_' as prefix. e.g. 'feature_pima.csv' for 'pima.csv'. 198 | 199 | Parameters: 200 | ----------- 201 | inpath : str 202 | Path containing training set. 203 | thresh : float 204 | A value in [0,1) used as threshold for selecting 'relevant' features. 205 | A negative value suggest the use of minimum SU[i,c] value as threshold. 206 | delim : str 207 | Character to be used to delimit input file. defaults to ',' 208 | header : bool 209 | Whether the input file contains a header line. default to False. 210 | classAt : int 211 | 0-based index of the class vector in the file. A value of -1 (default) 212 | suggest to use last column. 213 | """ 214 | if os.path.exists(inpath): 215 | try: 216 | print "Reading file. Please wait ..." 217 | if header: 218 | d = np.genfromtxt(inpath, delimiter=str(delim), skip_header=1) 219 | else: 220 | d = np.loadtxt(inpath, delimiter=delim) 221 | print "Success! Dimensions: {0} x {1}".format(d.shape[0], d.shape[1]) 222 | except Exception, e: 223 | print "Input file loading failed. Please check the file." 224 | print "Error:", e 225 | raise e 226 | exit() 227 | 228 | if classAt == -1: 229 | X = d[:, :d.shape[1]-1] 230 | y = d[:,-1] 231 | else: 232 | idx = np.arange(d.shape[1]) 233 | X = d[:, idx[idx != classAt]] 234 | y = d[:, classAt] 235 | 236 | try: 237 | print "Performing FCBF selection. Please wait ..." 238 | print 'X: {}, y: {}'.format(X.shape, len(y)) 239 | sbest = fcbf(X, y, thresh) 240 | print "Done!" 241 | print "\n#Features selected: {0}".format(len(sbest)) 242 | print "Selected feature indices:\n{0}".format(sbest) 243 | try: 244 | outpath = os.path.split(inpath)[0] \ 245 | + '/features_' + os.path.split(inpath)[1] 246 | np.savetxt(outpath, sbest, fmt="%0.8f,%d", newline="\n", \ 247 | header='SU, 0-based Feature') 248 | print "\nFile saved successfully. Path: {0}".format(outpath) 249 | except Exception, e: 250 | print "Error encountered while saving file:", e 251 | except Exception, e: 252 | print "Error:", e 253 | else: 254 | print "The file you specified does not exist." 255 | 256 | def main(): 257 | ## ================= PARAMS ================= 258 | inpath = '../data/bot_online_dataset.dat' 259 | delim = '\t' 260 | thresh = -1 # Negative value => minimum SU 261 | header = True 262 | classAt = -1 # -1: last, otherwise: 0-based index of class 263 | ## ========================================== 264 | 265 | fcbf_wrapper(os.path.abspath(inpath), thresh, delim, header, classAt) 266 | 267 | if __name__ == '__main__': 268 | if len(sys.argv) == 1: 269 | main() 270 | else: 271 | parser = argparse.ArgumentParser(description='Fast Correlation-Based Filter Selection (FCBF)') 272 | parser.add_argument('-inpath', metavar='', type=str, \ 273 | dest='inpath', help='Path to input file') 274 | parser.add_argument('-thresh', metavar='', type=float, \ 275 | dest='thresh', help='SU threshold') 276 | parser.add_argument('-delim', metavar='', type=str, \ 277 | dest='delim',help='File delimiter', default=',') 278 | parser.add_argument('-header', metavar='', type=bool, \ 279 | dest='header',help='Contains header?', default=False) 280 | parser.add_argument('-classAt', metavar='', type=int, \ 281 | dest='classAt',help='Index of class column', default=-1) 282 | 283 | args = parser.parse_args() 284 | 285 | fcbf_wrapper(os.path.abspath(args.inpath), args.thresh, \ 286 | args.delim.decode('string_escape'), args.header, args.classAt) 287 | 288 | -------------------------------------------------------------------------------- /src/utilities.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # encoding: utf-8 3 | """ 4 | utilities.py 5 | 6 | Created by Prashant Shiralkar on 2015-02-06. 7 | 8 | Utility methods to compute information-theoretic concepts such as 9 | entropy, information gain, symmetrical uncertainty. 10 | 11 | """ 12 | 13 | import sys 14 | import os 15 | import random 16 | import numpy as np 17 | import scipy as sp 18 | 19 | def entropy(vec, base=2): 20 | " Returns the empirical entropy H(X) in the input vector." 21 | _, vec = np.unique(vec, return_counts=True) 22 | prob_vec = np.array(vec/float(sum(vec))) 23 | if base == 2: 24 | logfn = np.log2 25 | elif base == 10: 26 | logfn = np.log10 27 | else: 28 | logfn = np.log 29 | return prob_vec.dot(-logfn(prob_vec)) 30 | 31 | def conditional_entropy(x, y): 32 | "Returns H(X|Y)." 33 | uy, uyc = np.unique(y, return_counts=True) 34 | prob_uyc = uyc/float(sum(uyc)) 35 | cond_entropy_x = np.array([entropy(x[y == v]) for v in uy]) 36 | return prob_uyc.dot(cond_entropy_x) 37 | 38 | def mutual_information(x, y): 39 | " Returns the information gain/mutual information [H(X)-H(X|Y)] between two random vars x & y." 40 | return entropy(x) - conditional_entropy(x, y) 41 | 42 | def symmetrical_uncertainty(x, y): 43 | " Returns 'symmetrical uncertainty' - a symmetric mutual information measure." 44 | return 2.0*mutual_information(x, y)/(entropy(x) + entropy(y)) 45 | 46 | if __name__ == '__main__': 47 | vec1 = np.linspace(1,20,20) 48 | print "Vec 1:", vec1 49 | print "Entropy:", entropy(vec1) 50 | 51 | vec2 = np.tile([4,5,6,7], 5) 52 | print "Vec 2:", vec2 53 | print "Entropy:", entropy(vec2) 54 | 55 | mi = mutual_information(vec1, vec2) 56 | print "Mutual information: {0}".format(mi) 57 | 58 | su = symmetrical_uncertainty(vec1, vec2) 59 | print "Symmetrical uncertainty: {0}". format(su) 60 | 61 | --------------------------------------------------------------------------------