diff options
Diffstat (limited to 'python/pyspark/mllib/regression.py')
| -rw-r--r-- | python/pyspark/mllib/regression.py | 52 |
1 files changed, 21 insertions, 31 deletions
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 93e17faf5c..43c1a2fc10 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -18,9 +18,8 @@ import numpy as np from numpy import array -from pyspark import SparkContext -from pyspark.serializers import PickleSerializer, AutoBatchedSerializer -from pyspark.mllib.linalg import SparseVector, _convert_to_vector, _to_java_object_rdd +from pyspark.mllib.common import callMLlibFunc, _to_java_object_rdd +from pyspark.mllib.linalg import SparseVector, _convert_to_vector __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel', 'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD'] @@ -124,17 +123,11 @@ class LinearRegressionModel(LinearRegressionModelBase): # train_func should take two parameters, namely data and initial_weights, and # return the result of a call to the appropriate JVM stub. # _regression_train_wrapper is responsible for setup and error checking. -def _regression_train_wrapper(sc, train_func, modelClass, data, initial_weights): +def _regression_train_wrapper(train_func, modelClass, data, initial_weights): initial_weights = initial_weights or [0.0] * len(data.first().features) - ser = PickleSerializer() - initial_bytes = bytearray(ser.dumps(_convert_to_vector(initial_weights))) - # use AutoBatchedSerializer before cache to reduce the memory - # overhead in JVM - cached = data._reserialize(AutoBatchedSerializer(ser)).cache() - ans = train_func(_to_java_object_rdd(cached), initial_bytes) - assert len(ans) == 2, "JVM call result had unexpected length" - weights = ser.loads(str(ans[0])) - return modelClass(weights, ans[1]) + weights, intercept = train_func(_to_java_object_rdd(data, cache=True), + _convert_to_vector(initial_weights)) + return modelClass(weights, intercept) class LinearRegressionWithSGD(object): @@ -168,13 +161,12 @@ class LinearRegressionWithSGD(object): training data (i.e. whether bias features are activated or not). """ - sc = data.context + def train(rdd, i): + return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, iterations, step, + miniBatchFraction, i, regParam, regType, intercept) - def train(jrdd, i): - return sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD( - jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept) - - return _regression_train_wrapper(sc, train, LinearRegressionModel, data, initialWeights) + return _regression_train_wrapper(train, LinearRegressionModel, + data, initialWeights) class LassoModel(LinearRegressionModelBase): @@ -216,12 +208,10 @@ class LassoWithSGD(object): def train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a Lasso regression model on the given data.""" - sc = data.context - - def train(jrdd, i): - return sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD( - jrdd, iterations, step, regParam, miniBatchFraction, i) - return _regression_train_wrapper(sc, train, LassoModel, data, initialWeights) + def train(rdd, i): + return callMLlibFunc("trainLassoModelWithSGD", rdd, iterations, step, regParam, + miniBatchFraction, i) + return _regression_train_wrapper(train, LassoModel, data, initialWeights) class RidgeRegressionModel(LinearRegressionModelBase): @@ -263,17 +253,17 @@ class RidgeRegressionWithSGD(object): def train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None): """Train a ridge regression model on the given data.""" - sc = data.context - - def train(jrdd, i): - return sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD( - jrdd, iterations, step, regParam, miniBatchFraction, i) + def train(rdd, i): + return callMLlibFunc("trainRidgeModelWithSGD", rdd, iterations, step, regParam, + miniBatchFraction, i) - return _regression_train_wrapper(sc, train, RidgeRegressionModel, data, initialWeights) + return _regression_train_wrapper(train, RidgeRegressionModel, + data, initialWeights) def _test(): import doctest + from pyspark import SparkContext globs = globals().copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) |