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-rw-r--r--mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java81
1 files changed, 81 insertions, 0 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
new file mode 100644
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--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
@@ -0,0 +1,81 @@
+/*
+ * 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 com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaHashingTFSuite {
+ private transient JavaSparkContext jsc;
+ private transient SQLContext jsql;
+
+ @Before
+ public void setUp() {
+ jsc = new JavaSparkContext("local", "JavaHashingTFSuite");
+ jsql = new SQLContext(jsc);
+ }
+
+ @After
+ public void tearDown() {
+ jsc.stop();
+ jsc = null;
+ }
+
+ @Test
+ public void hashingTF() {
+ JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+ RowFactory.create(0, "Hi I heard about Spark"),
+ RowFactory.create(0, "I wish Java could use case classes"),
+ RowFactory.create(1, "Logistic regression models are neat")
+ ));
+ StructType schema = new StructType(new StructField[]{
+ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+ new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
+ });
+ DataFrame sentenceDataFrame = jsql.createDataFrame(jrdd, schema);
+
+ Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
+ DataFrame wordsDataFrame = tokenizer.transform(sentenceDataFrame);
+ int numFeatures = 20;
+ HashingTF hashingTF = new HashingTF()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setNumFeatures(numFeatures);
+ DataFrame featurized = hashingTF.transform(wordsDataFrame);
+ for (Row r : featurized.select("features", "words", "label").take(3)) {
+ Vector features = r.getAs(0);
+ Assert.assertEquals(features.size(), numFeatures);
+ }
+ }
+}