fix mllib python doc warnings

python/pyspark/mllib/evaluation.py

@@ -334,11 +334,10 @@ def ndcgAt(self, k):
         """
         Compute the average NDCG value of all the queries, truncated at ranking position k.
         The discounted cumulative gain at position k is computed as:
-            sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
+        sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
         and the NDCG is obtained by dividing the DCG value on the ground truth set.
         In the current implementation, the relevance value is binary.
-
-        If a query has an empty ground truth set, zero will be used as ndcg together with
+        If a query has an empty ground truth set, zero will be used as NDCG together with
         a log warning.
         """
         return self.call("ndcgAt", int(k))

python/pyspark/mllib/fpm.py

@@ -61,12 +61,12 @@ class FPGrowth(object):
     def train(cls, data, minSupport=0.3, numPartitions=-1):
         """
         Computes an FP-Growth model that contains frequent itemsets.
-        :param data:            The input data set, each element
-                                contains a transaction.
-        :param minSupport:      The minimal support level
-                                (default: `0.3`).
-        :param numPartitions:   The number of partitions used by parallel
-                                FP-growth (default: same as input data).
+
+        :param data: The input data set, each element contains a
+            transaction.
+        :param minSupport: The minimal support level (default: `0.3`).
+        :param numPartitions: The number of partitions used by
+            parallel FP-growth (default: same as input data).
         """
         model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
         return FPGrowthModel(model)