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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.feature
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.attribute._
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLlibTestSparkContext
class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
test("params") {
ParamsSuite.checkParams(new RFormula())
}
test("transform numeric data") {
val formula = new RFormula().setFormula("id ~ v1 + v2")
val original = sqlContext.createDataFrame(
Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
val model = formula.fit(original)
val result = model.transform(original)
val resultSchema = model.transformSchema(original.schema)
val expected = sqlContext.createDataFrame(
Seq(
(0, 1.0, 3.0, Vectors.dense(1.0, 3.0), 0.0),
(2, 2.0, 5.0, Vectors.dense(2.0, 5.0), 2.0))
).toDF("id", "v1", "v2", "features", "label")
// TODO(ekl) make schema comparisons ignore metadata, to avoid .toString
assert(result.schema.toString == resultSchema.toString)
assert(resultSchema == expected.schema)
assert(result.collect() === expected.collect())
}
test("features column already exists") {
val formula = new RFormula().setFormula("y ~ x").setFeaturesCol("x")
val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
intercept[IllegalArgumentException] {
formula.fit(original)
}
intercept[IllegalArgumentException] {
formula.fit(original)
}
}
test("label column already exists") {
val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "y")
val model = formula.fit(original)
val resultSchema = model.transformSchema(original.schema)
assert(resultSchema.length == 3)
assert(resultSchema.toString == model.transform(original).schema.toString)
}
test("label column already exists but is not double type") {
val formula = new RFormula().setFormula("y ~ x").setLabelCol("y")
val original = sqlContext.createDataFrame(Seq((0, 1), (2, 2))).toDF("x", "y")
val model = formula.fit(original)
intercept[IllegalArgumentException] {
model.transformSchema(original.schema)
}
intercept[IllegalArgumentException] {
model.transform(original)
}
}
test("allow missing label column for test datasets") {
val formula = new RFormula().setFormula("y ~ x").setLabelCol("label")
val original = sqlContext.createDataFrame(Seq((0, 1.0), (2, 2.0))).toDF("x", "_not_y")
val model = formula.fit(original)
val resultSchema = model.transformSchema(original.schema)
assert(resultSchema.length == 3)
assert(!resultSchema.exists(_.name == "label"))
assert(resultSchema.toString == model.transform(original).schema.toString)
}
test("encodes string terms") {
val formula = new RFormula().setFormula("id ~ a + b")
val original = sqlContext.createDataFrame(
Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5))
).toDF("id", "a", "b")
val model = formula.fit(original)
val result = model.transform(original)
val resultSchema = model.transformSchema(original.schema)
val expected = sqlContext.createDataFrame(
Seq(
(1, "foo", 4, Vectors.dense(0.0, 1.0, 4.0), 1.0),
(2, "bar", 4, Vectors.dense(1.0, 0.0, 4.0), 2.0),
(3, "bar", 5, Vectors.dense(1.0, 0.0, 5.0), 3.0),
(4, "baz", 5, Vectors.dense(0.0, 0.0, 5.0), 4.0))
).toDF("id", "a", "b", "features", "label")
assert(result.schema.toString == resultSchema.toString)
assert(result.collect() === expected.collect())
}
test("attribute generation") {
val formula = new RFormula().setFormula("id ~ a + b")
val original = sqlContext.createDataFrame(
Seq((1, "foo", 4), (2, "bar", 4), (3, "bar", 5), (4, "baz", 5))
).toDF("id", "a", "b")
val model = formula.fit(original)
val result = model.transform(original)
val attrs = AttributeGroup.fromStructField(result.schema("features"))
val expectedAttrs = new AttributeGroup(
"features",
Array(
new BinaryAttribute(Some("a__bar"), Some(1)),
new BinaryAttribute(Some("a__foo"), Some(2)),
new NumericAttribute(Some("b"), Some(3))))
assert(attrs === expectedAttrs)
}
}
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