diff options
| -rw-r--r-- | python/docs/modules.rst | 7 | ||||
| -rw-r--r-- | python/pyspark/context.py | 1 | ||||
| -rw-r--r-- | python/pyspark/mllib/classification.py | 26 | ||||
| -rw-r--r-- | python/pyspark/mllib/regression.py | 15 | ||||
| -rw-r--r-- | python/pyspark/mllib/tree.py | 1 | ||||
| -rw-r--r-- | python/pyspark/rdd.py | 1 |
6 files changed, 28 insertions, 23 deletions
diff --git a/python/docs/modules.rst b/python/docs/modules.rst deleted file mode 100644 index 183564659f..0000000000 --- a/python/docs/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -. -= - -.. toctree:: - :maxdepth: 4 - - pyspark diff --git a/python/pyspark/context.py b/python/pyspark/context.py index e9418320ff..a45d79d642 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -410,6 +410,7 @@ class SparkContext(object): Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is as follows: + 1. A Java RDD is created from the SequenceFile or other InputFormat, and the key and value Writable classes 2. Serialization is attempted via Pyrolite pickling diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index ac142fb49a..a765b1c4f7 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -89,11 +89,14 @@ class LogisticRegressionWithSGD(object): @param regParam: The regularizer parameter (default: 1.0). @param regType: The type of regularizer used for training our model. - Allowed values: "l1" for using L1Updater, - "l2" for using - SquaredL2Updater, - "none" for no regularizer. - (default: "none") + + :Allowed values: + - "l1" for using L1Updater + - "l2" for using SquaredL2Updater + - "none" for no regularizer + + (default: "none") + @param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features @@ -158,11 +161,14 @@ class SVMWithSGD(object): @param initialWeights: The initial weights (default: None). @param regType: The type of regularizer used for training our model. - Allowed values: "l1" for using L1Updater, - "l2" for using - SquaredL2Updater, - "none" for no regularizer. - (default: "none") + + :Allowed values: + - "l1" for using L1Updater + - "l2" for using SquaredL2Updater, + - "none" for no regularizer. + + (default: "none") + @param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 8fe8c6db2a..54f34a9833 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -22,7 +22,7 @@ from pyspark import SparkContext from pyspark.mllib.linalg import SparseVector, _convert_to_vector from pyspark.serializers import PickleSerializer, AutoBatchedSerializer -__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel' +__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel', 'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD'] @@ -155,11 +155,14 @@ class LinearRegressionWithSGD(object): @param regParam: The regularizer parameter (default: 1.0). @param regType: The type of regularizer used for training our model. - Allowed values: "l1" for using L1Updater, - "l2" for using - SquaredL2Updater, - "none" for no regularizer. - (default: "none") + + :Allowed values: + - "l1" for using L1Updater, + - "l2" for using SquaredL2Updater, + - "none" for no regularizer. + + (default: "none") + @param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index afdcdbdf3a..5d7abfb96b 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -48,6 +48,7 @@ class DecisionTreeModel(object): def predict(self, x): """ Predict the label of one or more examples. + :param x: Data point (feature vector), or an RDD of data points (feature vectors). """ diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index dc6497772e..e77669aad7 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -1208,6 +1208,7 @@ class RDD(object): Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file system, using the L{org.apache.hadoop.io.Writable} types that we convert from the RDD's key and value types. The mechanism is as follows: + 1. Pyrolite is used to convert pickled Python RDD into RDD of Java objects. 2. Keys and values of this Java RDD are converted to Writables and written out. |