diff options
Diffstat (limited to 'python/pyspark/ml/feature.py')
-rwxr-xr-x | python/pyspark/ml/feature.py | 13 |
1 files changed, 7 insertions, 6 deletions
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 635cf13045..40b63d4d31 100755 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -742,8 +742,8 @@ class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, Jav For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min) - Note that since zero values will probably be transformed to non-zero values, output of the - transformer will be DenseVector even for sparse input. + .. note:: Since zero values will probably be transformed to non-zero values, output of the + transformer will be DenseVector even for sparse input. >>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"]) @@ -1014,9 +1014,9 @@ class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, :py:attr:`dropLast`) because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`. - Note that this is different from scikit-learn's OneHotEncoder, - which keeps all categories. - The output vectors are sparse. + + .. note:: This is different from scikit-learn's OneHotEncoder, + which keeps all categories. The output vectors are sparse. .. seealso:: @@ -1698,7 +1698,8 @@ class IndexToString(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ A feature transformer that filters out stop words from input. - Note: null values from input array are preserved unless adding null to stopWords explicitly. + + .. note:: null values from input array are preserved unless adding null to stopWords explicitly. >>> df = spark.createDataFrame([(["a", "b", "c"],)], ["text"]) >>> remover = StopWordsRemover(inputCol="text", outputCol="words", stopWords=["b"]) |