VectorIndexer
import org.apache.spark.ml.feature.VectorIndexer
val data = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_libsvm_data.txt")
val indexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexed")
.setMaxCategories(10)
val indexerModel = indexer.fit(data)
val categoricalFeatures: Set[Int] = indexerModel.categoryMaps.keys.toSet
println(s"Chose ${categoricalFeatures.size} " +
s"categorical features: ${categoricalFeatures.mkString(", ")}")
// Create new column "indexed" with categorical values transformed to indices
val indexedData = indexerModel.transform(data)
indexedData.show()
/*
Output:
Chose 351 categorical features: 645, 69, 365, 138, 101, 479, 333, 249, 0, 555, 666, 88, 170, 115, 276, 308, 5, 449, 120, 247, 614, 677, 202, 10, 56, 533, 142, 500, 340, 670, 174, 42, 417, 24, 37, 25, 257, 389, 52, 14, 504, 110, 587, 619, 196, 559, 638, 20, 421, 46, 93, 284, 228, 448, 57, 78, 29, 475, 164, 591, 646, 253, 106, 121, 84, 480, 147, 280, 61, 221, 396, 89, 133, 116, 1, 507, 312, 74, 307, 452, 6, 248, 60, 117, 678, 529, 85, 201, 220, 366, 534, 102, 334, 28, 38, 561, 392, 70, 424, 192, 21, 137, 165, 33, 92, 229, 252, 197, 361, 65, 97, 665, 583, 285, 224, 650, 615, 9, 53, 169, 593, 141, 610, 420, 109, 256, 225, 339, 77, 193, 669, 476, 642, 637, 590, 679, 96, 393, 647, 173, 13, 41, 503, 134, 73, 105, 2, 508, 311, 558, 674, 530, 586, 618, 166, 32, 34, 148, 45, 161, 279, 64, 689, 17, 149, 584, 562, 176, 423, 191, 22, 44, 59, 118, 281, 27, 641, 71, 391, 12, 445, 54, 313, 611, 144, 49, 335, 86, 672, 172, 113, 681, 219, 419, 81, 230, 362, 451, 76, 7, 39, 649, 98, 616, 477, 367, 535, 103, 140, 621, 91, 66, 251, 668, 198, 108, 278, 223, 394, 306, 135, 563, 226, 3, 505, 80, 167, 35, 473, 675, 589, 162, 531, 680, 255, 648, 112, 617, 194, 145, 48, 557, 690, 63, 640, 18, 282, 95, 310, 50, 67, 199, 673, 16, 585, 502, 338, 643, 31, 336, 613, 11, 72, 175, 446, 612, 143, 43, 250, 231, 450, 99, 363, 556, 87, 203, 671, 688, 104, 368, 588, 40, 304, 26, 258, 390, 55, 114, 171, 139, 418, 23, 8, 75, 119, 58, 667, 478, 536, 82, 620, 447, 36, 168, 146, 30, 51, 190, 19, 422, 564, 305, 107, 4, 136, 506, 79, 195, 474, 664, 532, 94, 283, 395, 332, 528, 644, 47, 15, 163, 200, 68, 62, 277, 691, 501, 90, 111, 254, 227, 337, 122, 83, 309, 560, 639, 676, 222, 592, 364, 100
+-----+--------------------+--------------------+
|label| features| indexed|
+-----+--------------------+--------------------+
| 0.0|(692,[127,128,129...|(692,[127,128,129...|
| 1.0|(692,[158,159,160...|(692,[158,159,160...|
| 1.0|(692,[124,125,126...|(692,[124,125,126...|
| 1.0|(692,[152,153,154...|(692,[152,153,154...|
| 1.0|(692,[151,152,153...|(692,[151,152,153...|
| 0.0|(692,[129,130,131...|(692,[129,130,131...|
| 1.0|(692,[158,159,160...|(692,[158,159,160...|
| 1.0|(692,[99,100,101,...|(692,[99,100,101,...|
| 0.0|(692,[154,155,156...|(692,[154,155,156...|
| 0.0|(692,[127,128,129...|(692,[127,128,129...|
| 1.0|(692,[154,155,156...|(692,[154,155,156...|
| 0.0|(692,[153,154,155...|(692,[153,154,155...|
| 0.0|(692,[151,152,153...|(692,[151,152,153...|
| 1.0|(692,[129,130,131...|(692,[129,130,131...|
| 0.0|(692,[154,155,156...|(692,[154,155,156...|
| 1.0|(692,[150,151,152...|(692,[150,151,152...|
| 0.0|(692,[124,125,126...|(692,[124,125,126...|
| 0.0|(692,[152,153,154...|(692,[152,153,154...|
| 1.0|(692,[97,98,99,12...|(692,[97,98,99,12...|
| 1.0|(692,[124,125,126...|(692,[124,125,126...|
+-----+--------------------+--------------------+
only showing top 20 rows
*/Last updated