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Posted to commits@spark.apache.org by ma...@apache.org on 2013/12/26 07:31:26 UTC
[09/28] git commit: Tests for the Python side of the mllib bindings.
Tests for the Python side of the mllib bindings.
Project: http://git-wip-us.apache.org/repos/asf/incubator-spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-spark/commit/2940201a
Tree: http://git-wip-us.apache.org/repos/asf/incubator-spark/tree/2940201a
Diff: http://git-wip-us.apache.org/repos/asf/incubator-spark/diff/2940201a
Branch: refs/heads/master
Commit: 2940201ad86e5dee16cf7386b3c934fc75c15582
Parents: 73e1706
Author: Tor Myklebust <tm...@gmail.com>
Authored: Fri Dec 20 01:33:32 2013 -0500
Committer: Tor Myklebust <tm...@gmail.com>
Committed: Fri Dec 20 01:33:32 2013 -0500
----------------------------------------------------------------------
python/pyspark/mllib.py | 224 +++++++++++++++++++++++++++++++++----------
1 file changed, 172 insertions(+), 52 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/2940201a/python/pyspark/mllib.py
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diff --git a/python/pyspark/mllib.py b/python/pyspark/mllib.py
index 21f3c03..aa9fc76 100644
--- a/python/pyspark/mllib.py
+++ b/python/pyspark/mllib.py
@@ -1,4 +1,5 @@
from numpy import *
+from pyspark import SparkContext
# Double vector format:
#
@@ -7,44 +8,106 @@ from numpy import *
# Double matrix format:
#
# [8-byte 2] [8-byte rows] [8-byte cols] [rows*cols*8 bytes of data]
-#
+#
# This is all in machine-endian. That means that the Java interpreter and the
# Python interpreter must agree on what endian the machine is.
def _deserialize_byte_array(shape, ba, offset):
+ """Wrapper around ndarray aliasing hack.
+
+ >>> x = array([1.0, 2.0, 3.0, 4.0, 5.0])
+ >>> array_equal(x, _deserialize_byte_array(x.shape, x.data, 0))
+ True
+ >>> x = array([1.0, 2.0, 3.0, 4.0]).reshape(2,2)
+ >>> array_equal(x, _deserialize_byte_array(x.shape, x.data, 0))
+ True
+ """
ar = ndarray(shape=shape, buffer=ba, offset=offset, dtype="float64",
order='C')
return ar.copy()
def _serialize_double_vector(v):
- if (type(v) == ndarray and v.dtype == float64 and v.ndim == 1):
- length = v.shape[0]
- ba = bytearray(16 + 8*length)
- header = ndarray(shape=[2], buffer=ba, dtype="int64")
- header[0] = 1
- header[1] = length
- copyto(ndarray(shape=[length], buffer=ba, offset=16,
- dtype="float64"), v)
- return ba
- else:
- raise TypeError("_serialize_double_vector called on a "
- "non-double-vector")
+ """Serialize a double vector into a mutually understood format.
+
+ >>> _serialize_double_vector(array([]))
+ bytearray(b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00')
+ >>> _serialize_double_vector(array([0.0, 1.0]))
+ bytearray(b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xf0?')
+ >>> _serialize_double_vector("hello, world")
+ Traceback (most recent call last):
+ File "/usr/lib/python2.7/doctest.py", line 1289, in __run
+ compileflags, 1) in test.globs
+ File "<doctest __main__._serialize_double_vector[1]>", line 1, in <module>
+ _serialize_double_vector("hello, world")
+ File "python/pyspark/mllib.py", line 41, in _serialize_double_vector
+ raise TypeError("_serialize_double_vector called on a %s; wanted ndarray" % type(v))
+ TypeError: _serialize_double_vector called on a <type 'str'>; wanted ndarray
+ >>> _serialize_double_vector(array([0, 1]))
+ Traceback (most recent call last):
+ File "/usr/lib/python2.7/doctest.py", line 1289, in __run
+ compileflags, 1) in test.globs
+ File "<doctest __main__._serialize_double_vector[2]>", line 1, in <module>
+ _serialize_double_vector(array([0, 1]))
+ File "python/pyspark/mllib.py", line 51, in _serialize_double_vector
+ "wanted ndarray of float64" % v.dtype)
+ TypeError: _serialize_double_vector called on an ndarray of int64; wanted ndarray of float64
+ >>> _serialize_double_vector(array([0.0, 1.0, 2.0, 3.0]).reshape(2,2))
+ Traceback (most recent call last):
+ File "/usr/lib/python2.7/doctest.py", line 1289, in __run
+ compileflags, 1) in test.globs
+ File "<doctest __main__._serialize_double_vector[3]>", line 1, in <module>
+ _serialize_double_vector(array([0.0, 1.0, 2.0, 3.0]).reshape(2,2))
+ File "python/pyspark/mllib.py", line 62, in _serialize_double_vector
+ "wanted a 1darray" % v.ndim)
+ TypeError: _serialize_double_vector called on a 2darray; wanted a 1darray
+ """
+ if type(v) != ndarray:
+ raise TypeError("_serialize_double_vector called on a %s; "
+ "wanted ndarray" % type(v))
+ if v.dtype != float64:
+ raise TypeError("_serialize_double_vector called on an ndarray of %s; "
+ "wanted ndarray of float64" % v.dtype)
+ if v.ndim != 1:
+ raise TypeError("_serialize_double_vector called on a %ddarray; "
+ "wanted a 1darray" % v.ndim)
+ length = v.shape[0]
+ ba = bytearray(16 + 8*length)
+ header = ndarray(shape=[2], buffer=ba, dtype="int64")
+ header[0] = 1
+ header[1] = length
+ copyto(ndarray(shape=[length], buffer=ba, offset=16,
+ dtype="float64"), v)
+ return ba
def _deserialize_double_vector(ba):
- if (type(ba) == bytearray and len(ba) >= 16 and (len(ba) & 7 == 0)):
- header = ndarray(shape=[2], buffer=ba, dtype="int64")
- if (header[0] != 1):
- raise TypeError("_deserialize_double_vector called on bytearray "
- "with wrong magic")
- length = header[1]
- if (len(ba) != 8*length + 16):
- raise TypeError("_deserialize_double_vector called on bytearray "
- "with wrong length")
- return _deserialize_byte_array([length], ba, 16)
- else:
- raise TypeError("_deserialize_double_vector called on a non-bytearray")
+ """Deserialize a double vector from a mutually understood format.
+
+ >>> x = array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0])
+ >>> array_equal(x, _deserialize_double_vector(_serialize_double_vector(x)))
+ True
+ """
+ if type(ba) != bytearray:
+ raise TypeError("_deserialize_double_vector called on a %s; "
+ "wanted bytearray" % type(ba))
+ if len(ba) < 16:
+ raise TypeError("_deserialize_double_vector called on a %d-byte array, "
+ "which is too short" % len(ba))
+ if (len(ba) & 7) != 0:
+ raise TypeError("_deserialize_double_vector called on a %d-byte array, "
+ "which is not a multiple of 8" % len(ba))
+ header = ndarray(shape=[2], buffer=ba, dtype="int64")
+ if header[0] != 1:
+ raise TypeError("_deserialize_double_vector called on bytearray "
+ "with wrong magic")
+ length = header[1]
+ if len(ba) != 8*length + 16:
+ raise TypeError("_deserialize_double_vector called on bytearray "
+ "with wrong length")
+ return _deserialize_byte_array([length], ba, 16)
def _serialize_double_matrix(m):
+ """Serialize a double matrix into a mutually understood format.
+ """
if (type(m) == ndarray and m.dtype == float64 and m.ndim == 2):
rows = m.shape[0]
cols = m.shape[1]
@@ -61,22 +124,31 @@ def _serialize_double_matrix(m):
"non-double-matrix")
def _deserialize_double_matrix(ba):
- if (type(ba) == bytearray and len(ba) >= 24 and (len(ba) & 7 == 0)):
- header = ndarray(shape=[3], buffer=ba, dtype="int64")
- if (header[0] != 2):
- raise TypeError("_deserialize_double_matrix called on bytearray "
- "with wrong magic")
- rows = header[1]
- cols = header[2]
- if (len(ba) != 8*rows*cols + 24):
- raise TypeError("_deserialize_double_matrix called on bytearray "
- "with wrong length")
- return _deserialize_byte_array([rows, cols], ba, 24)
- else:
- raise TypeError("_deserialize_double_matrix called on a non-bytearray")
+ """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))
+ if len(ba) < 24:
+ raise TypeError("_deserialize_double_matrix called on a %d-byte array, "
+ "which is too short" % len(ba))
+ if (len(ba) & 7) != 0:
+ raise TypeError("_deserialize_double_matrix called on a %d-byte array, "
+ "which is not a multiple of 8" % len(ba))
+ header = ndarray(shape=[3], buffer=ba, dtype="int64")
+ if (header[0] != 2):
+ raise TypeError("_deserialize_double_matrix called on bytearray "
+ "with wrong magic")
+ rows = header[1]
+ cols = header[2]
+ if (len(ba) != 8*rows*cols + 24):
+ raise TypeError("_deserialize_double_matrix called on bytearray "
+ "with wrong length")
+ return _deserialize_byte_array([rows, cols], ba, 24)
def _linear_predictor_typecheck(x, coeffs):
- """Predict the class of the vector x."""
+ """Check that x is a one-dimensional vector of the right shape.
+ This is a temporary hackaround until I actually implement bulk predict."""
if type(x) == ndarray:
if x.ndim == 1:
if x.shape == coeffs.shape:
@@ -98,12 +170,17 @@ class LinearModel(object):
self._intercept = intercept
class LinearRegressionModelBase(LinearModel):
- """A linear regression model."""
+ """A linear regression model.
+
+ >>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
+ >>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6
+ True
+ """
def predict(self, x):
"""Predict the value of the dependent variable given a vector x"""
"""containing values for the independent variables."""
- _linear_predictor_typecheck(x, _coeff)
- return dot(_coeff, x) + _intercept
+ _linear_predictor_typecheck(x, self._coeff)
+ return dot(self._coeff, x) + self._intercept
# Map a pickled Python RDD of numpy double vectors to a Java RDD of
# _serialized_double_vectors
@@ -145,7 +222,11 @@ def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
return klass(_deserialize_double_vector(ans[0]), ans[1]);
class LinearRegressionModel(LinearRegressionModelBase):
- """A linear regression model derived from a least-squares fit."""
+ """A linear regression model derived from a least-squares fit.
+
+ >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
+ >>> lrm = LinearRegressionModel.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+ """
@classmethod
def train(cls, sc, data, iterations=100, step=1.0,
mini_batch_fraction=1.0, initial_weights=None):
@@ -156,8 +237,12 @@ class LinearRegressionModel(LinearRegressionModelBase):
LinearRegressionModel, data, initial_weights)
class LassoModel(LinearRegressionModelBase):
- """A linear regression model derived from a least-squares fit with an """
- """l_1 penalty term."""
+ """A linear regression model derived from a least-squares fit with an
+ l_1 penalty term.
+
+ >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
+ >>> lrm = LassoModel.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+ """
@classmethod
def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
mini_batch_fraction=1.0, initial_weights=None):
@@ -168,8 +253,12 @@ class LassoModel(LinearRegressionModelBase):
LassoModel, data, initial_weights)
class RidgeRegressionModel(LinearRegressionModelBase):
- """A linear regression model derived from a least-squares fit with an """
- """l_2 penalty term."""
+ """A linear regression model derived from a least-squares fit with an
+ l_2 penalty term.
+
+ >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
+ >>> lrm = RidgeRegressionModel.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
+ """
@classmethod
def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
mini_batch_fraction=1.0, initial_weights=None):
@@ -180,7 +269,11 @@ class RidgeRegressionModel(LinearRegressionModelBase):
RidgeRegressionModel, data, initial_weights)
class LogisticRegressionModel(LinearModel):
- """A linear binary classification model derived from logistic regression."""
+ """A linear binary classification model derived from logistic regression.
+
+ >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
+ >>> lrm = LogisticRegressionModel.train(sc, sc.parallelize(data))
+ """
def predict(self, x):
_linear_predictor_typecheck(x, _coeff)
margin = dot(x, _coeff) + intercept
@@ -197,7 +290,11 @@ class LogisticRegressionModel(LinearModel):
LogisticRegressionModel, data, initial_weights)
class SVMModel(LinearModel):
- """A support vector machine."""
+ """A support vector machine.
+
+ >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
+ >>> svm = SVMModel.train(sc, sc.parallelize(data))
+ """
def predict(self, x):
_linear_predictor_typecheck(x, _coeff)
margin = dot(x, _coeff) + intercept
@@ -212,15 +309,24 @@ class SVMModel(LinearModel):
SVMModel, data, initial_weights)
class KMeansModel(object):
- """A clustering model derived from the k-means method."""
+ """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)
+ >>> clusters = KMeansModel.train(sc, sc.parallelize(data), 2, maxIterations=10, runs=30, initialization_mode="random")
+ >>> clusters.predict(array([0.0, 0.0])) == clusters.predict(array([1.0, 1.0]))
+ True
+ >>> clusters.predict(array([8.0, 9.0])) == clusters.predict(array([9.0, 8.0]))
+ True
+ >>> clusters = KMeansModel.train(sc, sc.parallelize(data), 2)
+ """
def __init__(self, centers_):
self.centers = centers_
def predict(self, x):
best = 0
best_distance = 1e75
- for i in range(0, centers.shape[0]):
- diff = x - centers[i]
+ for i in range(0, self.centers.shape[0]):
+ diff = x - self.centers[i]
distance = sqrt(dot(diff, diff))
if distance < best_distance:
best = i
@@ -239,3 +345,17 @@ class KMeansModel(object):
raise RuntimeError("JVM call result had first element of type "
+ type(ans[0]) + " which is not bytearray");
return KMeansModel(_deserialize_double_matrix(ans[0]));
+
+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)
+ globs['sc'].stop()
+ print failure_count,"failures among",test_count,"tests"
+ if failure_count:
+ exit(-1)
+
+if __name__ == "__main__":
+ _test()