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from numpy import *;
from pyspark.serializers import NoOpSerializer, FramedSerializer, \
BatchedSerializer, CloudPickleSerializer, pack_long
#__all__ = ["train_linear_regression_model"];
# Double vector format:
#
# [8-byte 1] [8-byte length] [length*8 bytes of data]
#
# 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):
"""Implementation detail. Do not use directly."""
ar = ndarray(shape=shape, buffer=ba, offset=offset, dtype="float64", \
order='C');
return ar.copy();
def serialize_double_vector(v):
"""Implementation detail. Do not use directly."""
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");
def deserialize_double_vector(ba):
"""Implementation detail. Do not use directly."""
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");
def serialize_double_matrix(m):
"""Implementation detail. Do not use directly."""
if (type(m) == ndarray and m.dtype == float64 and m.ndim == 2):
rows = m.shape[0];
cols = m.shape[1];
ba = bytearray(24 + 8 * rows * cols);
header = ndarray(shape=[3], buffer=ba, dtype="int64");
header[0] = 2;
header[1] = rows;
header[2] = cols;
copyto(ndarray(shape=[rows, cols], buffer=ba, offset=24, dtype="float64", \
order='C'), m);
return ba;
else:
print type(m);
print m.dtype;
print m.ndim;
raise TypeError("serialize_double_matrix called on a non-double-matrix");
def deserialize_double_matrix(ba):
"""Implementation detail. Do not use directly."""
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");
class LinearRegressionModel:
_coeff = None;
_intercept = None;
def __init__(self, coeff, intercept):
self._coeff = coeff;
self._intercept = intercept;
def predict(self, x):
if (type(x) == ndarray):
if (x.ndim == 1):
return dot(_coeff, x) - _intercept;
else:
raise RuntimeError("Bulk predict not yet supported.");
elif (type(x) == RDD):
raise RuntimeError("Bulk predict not yet supported.");
else:
raise TypeError("Bad type argument to LinearRegressionModel::predict");
def train_linear_regression_model(sc, data):
"""Train a linear regression model on the given data."""
dataBytes = data.map(serialize_double_vector);
sc.serializer = NoOpSerializer();
dataBytes.cache();
api = sc._jvm.PythonMLLibAPI();
ans = api.trainLinearRegressionModel(dataBytes._jrdd);
if (len(ans) != 2 or type(ans[0]) != bytearray or type(ans[1]) != float):
raise RuntimeError("train_linear_regression_model received garbage " \
"from JVM");
return LinearRegressionModel(deserialize_double_vector(ans[0]), ans[1]);
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