[SPARK-15150][EXAMPLE][DOC] Update LDA examples

## What changes were proposed in this pull request?
1,create a libsvm-type dataset for lda: `data/mllib/sample_lda_libsvm_data.txt`
2,add python example
3,directly read the datafile in examples
4,BTW, change to `SparkSession` in `aft_survival_regression.py`

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/lda_example.py`

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12927 from zhengruifeng/lda_pe.

2 files changed, 77 insertions, 6 deletions

diff --git a/examples/src/main/python/ml/aft_survival_regression.py b/examples/src/main/python/ml/aft_survival_regression.py
index 0ee01fd825..9879679829 100644
--- a/examples/src/main/python/ml/aft_survival_regression.py
+++ b/examples/src/main/python/ml/aft_survival_regression.py
@@ -17,19 +17,26 @@
 
 from __future__ import print_function
 
-from pyspark import SparkContext
-from pyspark.sql import SQLContext
 # $example on$
 from pyspark.ml.regression import AFTSurvivalRegression
 from pyspark.mllib.linalg import Vectors
 # $example off$
+from pyspark.sql import SparkSession
+
+"""
+An example demonstrating aft survival regression.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py
+"""
 
 if __name__ == "__main__":
-    sc = SparkContext(appName="AFTSurvivalRegressionExample")
-    sqlContext = SQLContext(sc)
+    spark = SparkSession \
+        .builder \
+        .appName("PythonAFTSurvivalRegressionExample") \
+        .getOrCreate()
 
     # $example on$
-    training = sqlContext.createDataFrame([
+    training = spark.createDataFrame([
         (1.218, 1.0, Vectors.dense(1.560, -0.605)),
         (2.949, 0.0, Vectors.dense(0.346, 2.158)),
         (3.627, 0.0, Vectors.dense(1.380, 0.231)),
@@ -48,4 +55,4 @@ if __name__ == "__main__":
     model.transform(training).show(truncate=False)
     # $example off$
 
-    sc.stop()
+    spark.stop()
diff --git a/examples/src/main/python/ml/lda_example.py b/examples/src/main/python/ml/lda_example.py
new file mode 100644
index 0000000000..6ca56adf3c
--- /dev/null
+++ b/examples/src/main/python/ml/lda_example.py
@@ -0,0 +1,64 @@
+#
+# 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.
+#
+
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.clustering import LDA
+# $example off$
+from pyspark.sql import SparkSession
+
+
+"""
+An example demonstrating LDA.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/lda_example.py
+"""
+
+
+if __name__ == "__main__":
+    # Creates a SparkSession
+    spark = SparkSession \
+        .builder \
+        .appName("PythonKMeansExample") \
+        .getOrCreate()
+
+    # $example on$
+    # Loads data.
+    dataset = spark.read.format("libsvm").load("data/mllib/sample_lda_libsvm_data.txt")
+
+    # Trains a LDA model.
+    lda = LDA(k=10, maxIter=10)
+    model = lda.fit(dataset)
+
+    ll = model.logLikelihood(dataset)
+    lp = model.logPerplexity(dataset)
+    print("The lower bound on the log likelihood of the entire corpus: " + str(ll))
+    print("The upper bound bound on perplexity: " + str(lp))
+
+    # Describe topics.
+    topics = model.describeTopics(3)
+    print("The topics described by their top-weighted terms:")
+    topics.show(truncate=False)
+
+    # Shows the result
+    transformed = model.transform(dataset)
+    transformed.show(truncate=False)
+    # $example off$
+
+    spark.stop()