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authorPravin Gadakh <pravingadakh177@gmail.com>2015-11-04 08:32:08 -0800
committerXiangrui Meng <meng@databricks.com>2015-11-04 08:32:08 -0800
commit820064e613609bbf7edd726d982da1de60bf417a (patch)
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parente328b69c31821e4b27673d7ef6182ab3b7a05ca8 (diff)
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[SPARK-11380][DOCS] Replace example code in mllib-frequent-pattern-mining.md using include_example
Author: Pravin Gadakh <pravingadakh177@gmail.com> Author: Pravin Gadakh <prgadakh@in.ibm.com> Closes #9340 from pravingadakh/SPARK-11380.
Diffstat (limited to 'examples/src/main/python')
-rw-r--r--examples/src/main/python/mllib/fpgrowth_example.py33
1 files changed, 33 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/fpgrowth_example.py b/examples/src/main/python/mllib/fpgrowth_example.py
new file mode 100644
index 0000000000..715f526820
--- /dev/null
+++ b/examples/src/main/python/mllib/fpgrowth_example.py
@@ -0,0 +1,33 @@
+#
+# 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.
+#
+
+# $example on$
+from pyspark.mllib.fpm import FPGrowth
+# $example off$
+from pyspark import SparkContext
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="FPGrowth")
+
+ # $example on$
+ data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+ transactions = data.map(lambda line: line.strip().split(' '))
+ model = FPGrowth.train(transactions, minSupport=0.2, numPartitions=10)
+ result = model.freqItemsets().collect()
+ for fi in result:
+ print(fi)
+ # $example off$