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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.python
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.linalg.{DenseMatrix, Matrices, SparseMatrix, Vectors}
class MLSerDeSuite extends SparkFunSuite {
MLSerDe.initialize()
test("pickle vector") {
val vectors = Seq(
Vectors.dense(Array.empty[Double]),
Vectors.dense(0.0),
Vectors.dense(0.0, -2.0),
Vectors.sparse(0, Array.empty[Int], Array.empty[Double]),
Vectors.sparse(1, Array.empty[Int], Array.empty[Double]),
Vectors.sparse(2, Array(1), Array(-2.0)))
vectors.foreach { v =>
val u = MLSerDe.loads(MLSerDe.dumps(v))
assert(u.getClass === v.getClass)
assert(u === v)
}
}
test("pickle double") {
for (x <- List(123.0, -10.0, 0.0, Double.MaxValue, Double.MinValue, Double.NaN)) {
val deser = MLSerDe.loads(MLSerDe.dumps(x.asInstanceOf[AnyRef])).asInstanceOf[Double]
// We use `equals` here for comparison because we cannot use `==` for NaN
assert(x.equals(deser))
}
}
test("pickle matrix") {
val values = Array[Double](0, 1.2, 3, 4.56, 7, 8)
val matrix = Matrices.dense(2, 3, values)
val nm = MLSerDe.loads(MLSerDe.dumps(matrix)).asInstanceOf[DenseMatrix]
assert(matrix === nm)
// Test conversion for empty matrix
val empty = Array.empty[Double]
val emptyMatrix = Matrices.dense(0, 0, empty)
val ne = MLSerDe.loads(MLSerDe.dumps(emptyMatrix)).asInstanceOf[DenseMatrix]
assert(emptyMatrix == ne)
val sm = new SparseMatrix(3, 2, Array(0, 1, 3), Array(1, 0, 2), Array(0.9, 1.2, 3.4))
val nsm = MLSerDe.loads(MLSerDe.dumps(sm)).asInstanceOf[SparseMatrix]
assert(sm.toArray === nsm.toArray)
val smt = new SparseMatrix(
3, 3, Array(0, 2, 3, 5), Array(0, 2, 1, 0, 2), Array(0.9, 1.2, 3.4, 5.7, 8.9),
isTransposed = true)
val nsmt = MLSerDe.loads(MLSerDe.dumps(smt)).asInstanceOf[SparseMatrix]
assert(smt.toArray === nsmt.toArray)
}
}
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