diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e6f0953810ed74a1c9e7e73668147952687580cc..802a27a8da14d362e954e7bf4ee94134c3805ac0 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -56,7 +56,8 @@ except:
#
# Sparse double vector format:
#
-# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] [nonzeros*8 bytes of values]
+# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] \
+# [nonzeros*8 bytes of values]
#
# Double matrix format:
#
@@ -110,18 +111,18 @@ def _serialize_double_vector(v):
return _serialize_sparse_vector(v)
else:
raise TypeError("_serialize_double_vector called on a %s; "
- "wanted ndarray or SparseVector" % type(v))
+ "wanted ndarray or SparseVector" % type(v))
def _serialize_dense_vector(v):
"""Serialize a dense vector given as a NumPy array."""
if v.ndim != 1:
raise TypeError("_serialize_double_vector called on a %ddarray; "
- "wanted a 1darray" % v.ndim)
+ "wanted a 1darray" % v.ndim)
if v.dtype != float64:
if numpy.issubdtype(v.dtype, numpy.complex):
raise TypeError("_serialize_double_vector called on an ndarray of %s; "
- "wanted ndarray of float64" % v.dtype)
+ "wanted ndarray of float64" % v.dtype)
v = v.astype(float64)
length = v.shape[0]
ba = bytearray(5 + 8 * length)
@@ -158,10 +159,10 @@ def _deserialize_double_vector(ba):
"""
if type(ba) != bytearray:
raise TypeError("_deserialize_double_vector called on a %s; "
- "wanted bytearray" % type(ba))
+ "wanted bytearray" % type(ba))
if len(ba) < 5:
raise TypeError("_deserialize_double_vector called on a %d-byte array, "
- "which is too short" % len(ba))
+ "which is too short" % len(ba))
if ba[0] == DENSE_VECTOR_MAGIC:
return _deserialize_dense_vector(ba)
elif ba[0] == SPARSE_VECTOR_MAGIC:
@@ -175,7 +176,7 @@ def _deserialize_dense_vector(ba):
"""Deserialize a dense vector into a numpy array."""
if len(ba) < 5:
raise TypeError("_deserialize_dense_vector called on a %d-byte array, "
- "which is too short" % len(ba))
+ "which is too short" % len(ba))
length = ndarray(shape=[1], buffer=ba, offset=1, dtype=int32)[0]
if len(ba) != 8 * length + 5:
raise TypeError("_deserialize_dense_vector called on bytearray "
@@ -187,7 +188,7 @@ def _deserialize_sparse_vector(ba):
"""Deserialize a sparse vector into a MLlib SparseVector object."""
if len(ba) < 9:
raise TypeError("_deserialize_sparse_vector called on a %d-byte array, "
- "which is too short" % len(ba))
+ "which is too short" % len(ba))
header = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32)
size = header[0]
nonzeros = header[1]
@@ -205,7 +206,7 @@ def _serialize_double_matrix(m):
if m.dtype != float64:
if numpy.issubdtype(m.dtype, numpy.complex):
raise TypeError("_serialize_double_matrix called on an ndarray of %s; "
- "wanted ndarray of float64" % m.dtype)
+ "wanted ndarray of float64" % m.dtype)
m = m.astype(float64)
rows = m.shape[0]
cols = m.shape[1]
@@ -225,10 +226,10 @@ def _deserialize_double_matrix(ba):
"""Deserialize a double matrix from a mutually understood format."""
if type(ba) != bytearray:
raise TypeError("_deserialize_double_matrix called on a %s; "
- "wanted bytearray" % type(ba))
+ "wanted bytearray" % type(ba))
if len(ba) < 9:
raise TypeError("_deserialize_double_matrix called on a %d-byte array, "
- "which is too short" % len(ba))
+ "which is too short" % len(ba))
if ba[0] != DENSE_MATRIX_MAGIC:
raise TypeError("_deserialize_double_matrix called on bytearray "
"with wrong magic")
@@ -267,7 +268,7 @@ def _copyto(array, buffer, offset, shape, dtype):
def _get_unmangled_rdd(data, serializer):
dataBytes = data.map(serializer)
dataBytes._bypass_serializer = True
- dataBytes.cache() # TODO: users should unpersist() this later!
+ dataBytes.cache() # TODO: users should unpersist() this later!
return dataBytes
@@ -293,14 +294,14 @@ def _linear_predictor_typecheck(x, coeffs):
if type(x) == ndarray:
if x.ndim == 1:
if x.shape != coeffs.shape:
- raise RuntimeError("Got array of %d elements; wanted %d"
- % (numpy.shape(x)[0], coeffs.shape[0]))
+ raise RuntimeError("Got array of %d elements; wanted %d" % (
+ numpy.shape(x)[0], coeffs.shape[0]))
else:
raise RuntimeError("Bulk predict not yet supported.")
elif type(x) == SparseVector:
if x.size != coeffs.shape[0]:
- raise RuntimeError("Got sparse vector of size %d; wanted %d"
- % (x.size, coeffs.shape[0]))
+ raise RuntimeError("Got sparse vector of size %d; wanted %d" % (
+ x.size, coeffs.shape[0]))
elif (type(x) == RDD):
raise RuntimeError("Bulk predict not yet supported.")
else:
@@ -315,7 +316,7 @@ def _get_initial_weights(initial_weights, data):
if type(initial_weights) == ndarray:
if initial_weights.ndim != 1:
raise TypeError("At least one data element has "
- + initial_weights.ndim + " dimensions, which is not 1")
+ + initial_weights.ndim + " dimensions, which is not 1")
initial_weights = numpy.zeros([initial_weights.shape[0]])
elif type(initial_weights) == SparseVector:
initial_weights = numpy.zeros([initial_weights.size])
@@ -333,10 +334,10 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
raise RuntimeError("JVM call result had unexpected length")
elif type(ans[0]) != bytearray:
raise RuntimeError("JVM call result had first element of type "
- + type(ans[0]).__name__ + " which is not bytearray")
+ + type(ans[0]).__name__ + " which is not bytearray")
elif type(ans[1]) != float:
raise RuntimeError("JVM call result had second element of type "
- + type(ans[0]).__name__ + " which is not float")
+ + type(ans[0]).__name__ + " which is not float")
return klass(_deserialize_double_vector(ans[0]), ans[1])
@@ -450,8 +451,7 @@ def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
- (failure_count, test_count) = doctest.testmod(globs=globs,
- optionflags=doctest.ELLIPSIS)
+ (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 6772e4337ef3996814637cddf7152e9db2e3da4f..1c0c536c4fb3d5090902faf4ba80522c3ae0fb9d 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -29,6 +29,7 @@ from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint, LinearModel
from math import exp, log
+
class LogisticRegressionModel(LinearModel):
"""A linear binary classification model derived from logistic regression.
@@ -68,14 +69,14 @@ class LogisticRegressionModel(LinearModel):
class LogisticRegressionWithSGD(object):
@classmethod
- def train(cls, data, iterations=100, step=1.0,
- miniBatchFraction=1.0, initialWeights=None):
+ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None):
"""Train a logistic regression model on the given data."""
sc = data.context
- return _regression_train_wrapper(sc, lambda d, i:
- sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
- iterations, step, miniBatchFraction, i),
- LogisticRegressionModel, data, initialWeights)
+ train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
+ d._jrdd, iterations, step, miniBatchFraction, i)
+ return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
+ initialWeights)
+
class SVMModel(LinearModel):
"""A support vector machine.
@@ -106,16 +107,17 @@ class SVMModel(LinearModel):
margin = _dot(x, self._coeff) + self._intercept
return 1 if margin >= 0 else 0
+
class SVMWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a support vector machine on the given data."""
sc = data.context
- return _regression_train_wrapper(sc, lambda d, i:
- sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
- iterations, step, regParam, miniBatchFraction, i),
- SVMModel, data, initialWeights)
+ train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
+ d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+ return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)
+
class NaiveBayesModel(object):
"""
@@ -156,6 +158,7 @@ class NaiveBayesModel(object):
"""Return the most likely class for a data vector x"""
return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
+
class NaiveBayes(object):
@classmethod
def train(cls, data, lambda_=1.0):
@@ -186,8 +189,7 @@ def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
- (failure_count, test_count) = doctest.testmod(globs=globs,
- optionflags=doctest.ELLIPSIS)
+ (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index f65088c9170e0c218e7c599b1674c1027bb6fb5a..b380e8f6c8725f0aaadf74fd2f82e93b6aad444a 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -30,7 +30,8 @@ class KMeansModel(object):
"""A clustering model derived from the k-means method.
>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
- >>> model = KMeans.train(sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
+ >>> model = KMeans.train(
+ ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
True
>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
@@ -76,18 +77,17 @@ class KMeansModel(object):
class KMeans(object):
@classmethod
- def train(cls, data, k, maxIterations=100, runs=1,
- initializationMode="k-means||"):
+ def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
"""Train a k-means clustering model."""
sc = data.context
dataBytes = _get_unmangled_double_vector_rdd(data)
- ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd,
- k, maxIterations, runs, initializationMode)
+ ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
+ dataBytes._jrdd, k, maxIterations, runs, initializationMode)
if len(ans) != 1:
raise RuntimeError("JVM call result had unexpected length")
elif type(ans[0]) != bytearray:
raise RuntimeError("JVM call result had first element of type "
- + type(ans[0]) + " which is not bytearray")
+ + type(ans[0]) + " which is not bytearray")
matrix = _deserialize_double_matrix(ans[0])
return KMeansModel([row for row in matrix])
@@ -96,8 +96,7 @@ def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
- (failure_count, test_count) = doctest.testmod(globs=globs,
- optionflags=doctest.ELLIPSIS)
+ (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 7511ca7573ddb3a32ee26c3f85be6dcb7a5e5d38..276684272068bc9968e8f0bb56cdb1b557188724 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -54,7 +54,7 @@ class SparseVector(object):
if len(args) == 1:
pairs = args[0]
if type(pairs) == dict:
- pairs = pairs.items()
+ pairs = pairs.items()
pairs = sorted(pairs)
self.indices = array([p[0] for p in pairs], dtype=int32)
self.values = array([p[1] for p in pairs], dtype=float64)
@@ -88,7 +88,7 @@ class SparseVector(object):
result += self.values[i] * other[self.indices[i]]
return result
elif other.ndim == 2:
- results = [self.dot(other[:,i]) for i in xrange(other.shape[1])]
+ results = [self.dot(other[:, i]) for i in xrange(other.shape[1])]
return array(results)
else:
raise Exception("Cannot call dot with %d-dimensional array" % other.ndim)
@@ -135,7 +135,7 @@ class SparseVector(object):
return result
else:
raise Exception("Cannot call squared_distance with %d-dimensional array" %
- other.ndim)
+ other.ndim)
else:
result = 0.0
i, j = 0, 0
@@ -184,15 +184,14 @@ class SparseVector(object):
"""
return (isinstance(other, self.__class__)
- and other.size == self.size
- and array_equal(other.indices, self.indices)
- and array_equal(other.values, self.values))
+ and other.size == self.size
+ and array_equal(other.indices, self.indices)
+ and array_equal(other.values, self.values))
def __ne__(self, other):
return not self.__eq__(other)
-
class Vectors(object):
"""
Factory methods for working with vectors. Note that dense vectors
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index f4a83f0209e27ce9f5137490c23a7f610ce9a5d5..6c385042ffa5f7433b449ee611eae5fe06048e4d 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -24,6 +24,7 @@ from pyspark.mllib._common import \
_serialize_tuple, RatingDeserializer
from pyspark.rdd import RDD
+
class MatrixFactorizationModel(object):
"""A matrix factorisation model trained by regularized alternating
least-squares.
@@ -55,32 +56,34 @@ class MatrixFactorizationModel(object):
return RDD(self._java_model.predict(usersProductsJRDD._jrdd),
self._context, RatingDeserializer())
+
class ALS(object):
@classmethod
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
sc = ratings.context
ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
- mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd,
- rank, iterations, lambda_, blocks)
+ mod = sc._jvm.PythonMLLibAPI().trainALSModel(
+ ratingBytes._jrdd, rank, iterations, lambda_, blocks)
return MatrixFactorizationModel(sc, mod)
@classmethod
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
sc = ratings.context
ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
- mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd,
- rank, iterations, lambda_, blocks, alpha)
+ mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
+ ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha)
return MatrixFactorizationModel(sc, mod)
+
def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
- (failure_count, test_count) = doctest.testmod(globs=globs,
- optionflags=doctest.ELLIPSIS)
+ (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
+
if __name__ == "__main__":
_test()
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 266b31d3fab0e64ead3e69d698cafa3ab39f4969..bc7de6d2e89583857cbb15e8175fd39b1b8a2617 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -113,10 +113,9 @@ class LinearRegressionWithSGD(object):
miniBatchFraction=1.0, initialWeights=None):
"""Train a linear regression model on the given data."""
sc = data.context
- return _regression_train_wrapper(sc, lambda d, i:
- sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
- d._jrdd, iterations, step, miniBatchFraction, i),
- LinearRegressionModel, data, initialWeights)
+ train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
+ d._jrdd, iterations, step, miniBatchFraction, i)
+ return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)
class LassoModel(LinearRegressionModelBase):
@@ -157,10 +156,9 @@ class LassoWithSGD(object):
miniBatchFraction=1.0, initialWeights=None):
"""Train a Lasso regression model on the given data."""
sc = data.context
- return _regression_train_wrapper(sc, lambda d, i:
- sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
- iterations, step, regParam, miniBatchFraction, i),
- LassoModel, data, initialWeights)
+ train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
+ d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+ return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights)
class RidgeRegressionModel(LinearRegressionModelBase):
@@ -201,18 +199,16 @@ class RidgeRegressionWithSGD(object):
miniBatchFraction=1.0, initialWeights=None):
"""Train a ridge regression model on the given data."""
sc = data.context
- return _regression_train_wrapper(sc, lambda d, i:
- sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
- iterations, step, regParam, miniBatchFraction, i),
- RidgeRegressionModel, data, initialWeights)
+ train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
+ d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+ return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)
def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
- (failure_count, test_count) = doctest.testmod(globs=globs,
- optionflags=doctest.ELLIPSIS)
+ (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 1ee96bb4af37b8def7f8402f248c8c6132eabe71..37ccf1d59074379504627358c1b979568c8d5134 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -23,7 +23,7 @@ from numpy import array, array_equal
import unittest
from pyspark.mllib._common import _convert_vector, _serialize_double_vector, \
- _deserialize_double_vector, _dot, _squared_distance
+ _deserialize_double_vector, _dot, _squared_distance
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint
from pyspark.tests import PySparkTestCase
@@ -46,12 +46,9 @@ class VectorTests(unittest.TestCase):
self.assertTrue(sv is _convert_vector(sv))
self.assertTrue(dv is _convert_vector(dv))
self.assertTrue(array_equal(dv, _convert_vector(lst)))
- self.assertEquals(sv,
- _deserialize_double_vector(_serialize_double_vector(sv)))
- self.assertTrue(array_equal(dv,
- _deserialize_double_vector(_serialize_double_vector(dv))))
- self.assertTrue(array_equal(dv,
- _deserialize_double_vector(_serialize_double_vector(lst))))
+ self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(sv)))
+ self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(dv))))
+ self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(lst))))
def test_dot(self):
sv = SparseVector(4, {1: 1, 3: 2})
@@ -132,7 +129,7 @@ class ListTests(PySparkTestCase):
def test_regression(self):
from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
- RidgeRegressionWithSGD
+ RidgeRegressionWithSGD
data = [
LabeledPoint(-1.0, [0, -1]),
LabeledPoint(1.0, [0, 1]),
@@ -179,14 +176,10 @@ class SciPyTests(PySparkTestCase):
self.assertEquals(sv, _convert_vector(lil.tocoo()))
self.assertEquals(sv, _convert_vector(lil.tocsr()))
self.assertEquals(sv, _convert_vector(lil.todok()))
- self.assertEquals(sv,
- _deserialize_double_vector(_serialize_double_vector(lil)))
- self.assertEquals(sv,
- _deserialize_double_vector(_serialize_double_vector(lil.tocsc())))
- self.assertEquals(sv,
- _deserialize_double_vector(_serialize_double_vector(lil.tocsr())))
- self.assertEquals(sv,
- _deserialize_double_vector(_serialize_double_vector(lil.todok())))
+ self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil)))
+ self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsc())))
+ self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsr())))
+ self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.todok())))
def test_dot(self):
from scipy.sparse import lil_matrix
@@ -265,7 +258,7 @@ class SciPyTests(PySparkTestCase):
def test_regression(self):
from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
- RidgeRegressionWithSGD
+ RidgeRegressionWithSGD
data = [
LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 50d0cdd087625d91df2556d9b71c856c2c800805..0e5f4520b94028ce379e6e2857ff3032f443907c 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -21,6 +21,7 @@ from pyspark.mllib.linalg import Vectors, SparseVector
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib._common import _convert_vector
+
class MLUtils:
"""
Helper methods to load, save and pre-process data used in MLlib.
@@ -44,7 +45,6 @@ class MLUtils:
values[i] = float(value)
return label, indices, values
-
@staticmethod
def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
@@ -62,7 +62,6 @@ class MLUtils:
" but got " % type(v))
return " ".join(items)
-
@staticmethod
def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None):
"""
@@ -135,7 +134,6 @@ class MLUtils:
numFeatures = parsed.map(lambda x: 0 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
-
@staticmethod
def saveAsLibSVMFile(data, dir):
"""