spark-instrumented-optimizer/python/pyspark/mllib.py

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]);