6481d27425
Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs. Author: Davies Liu <davies.liu@gmail.com> Closes #2205 from davies/public and squashes the following commits: c6c5567 [Davies Liu] fix message f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module 7e3016a [Davies Liu] add __all__ in mllib 6281b48 [Davies Liu] fix doc for SchemaRDD 6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
278 lines
9.6 KiB
Python
278 lines
9.6 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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MLlib utilities for linear algebra. For dense vectors, MLlib
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uses the NumPy C{array} type, so you can simply pass NumPy arrays
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around. For sparse vectors, users can construct a L{SparseVector}
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object from MLlib or pass SciPy C{scipy.sparse} column vectors if
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SciPy is available in their environment.
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"""
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import numpy
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from numpy import array, array_equal, ndarray, float64, int32
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__all__ = ['SparseVector', 'Vectors']
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class SparseVector(object):
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"""
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A simple sparse vector class for passing data to MLlib. Users may
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alternatively pass SciPy's {scipy.sparse} data types.
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"""
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def __init__(self, size, *args):
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"""
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Create a sparse vector, using either a dictionary, a list of
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(index, value) pairs, or two separate arrays of indices and
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values (sorted by index).
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@param size: Size of the vector.
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@param args: Non-zero entries, as a dictionary, list of tupes,
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or two sorted lists containing indices and values.
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>>> print SparseVector(4, {1: 1.0, 3: 5.5})
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(4,[1,3],[1.0,5.5])
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>>> print SparseVector(4, [(1, 1.0), (3, 5.5)])
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(4,[1,3],[1.0,5.5])
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>>> print SparseVector(4, [1, 3], [1.0, 5.5])
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(4,[1,3],[1.0,5.5])
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"""
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self.size = int(size)
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assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
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if len(args) == 1:
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pairs = args[0]
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if type(pairs) == dict:
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pairs = pairs.items()
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pairs = sorted(pairs)
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self.indices = array([p[0] for p in pairs], dtype=int32)
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self.values = array([p[1] for p in pairs], dtype=float64)
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else:
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assert len(args[0]) == len(args[1]), "index and value arrays not same length"
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self.indices = array(args[0], dtype=int32)
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self.values = array(args[1], dtype=float64)
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for i in xrange(len(self.indices) - 1):
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if self.indices[i] >= self.indices[i + 1]:
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raise TypeError("indices array must be sorted")
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def dot(self, other):
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"""
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Dot product with a SparseVector or 1- or 2-dimensional Numpy array.
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>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
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>>> a.dot(a)
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25.0
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>>> a.dot(array([1., 2., 3., 4.]))
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22.0
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>>> b = SparseVector(4, [2, 4], [1.0, 2.0])
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>>> a.dot(b)
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0.0
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>>> a.dot(array([[1, 1], [2, 2], [3, 3], [4, 4]]))
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array([ 22., 22.])
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"""
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if type(other) == ndarray:
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if other.ndim == 1:
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result = 0.0
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for i in xrange(len(self.indices)):
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result += self.values[i] * other[self.indices[i]]
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return result
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elif other.ndim == 2:
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results = [self.dot(other[:, i]) for i in xrange(other.shape[1])]
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return array(results)
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else:
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raise Exception("Cannot call dot with %d-dimensional array" % other.ndim)
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else:
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result = 0.0
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i, j = 0, 0
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while i < len(self.indices) and j < len(other.indices):
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if self.indices[i] == other.indices[j]:
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result += self.values[i] * other.values[j]
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i += 1
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j += 1
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elif self.indices[i] < other.indices[j]:
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i += 1
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else:
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j += 1
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return result
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def squared_distance(self, other):
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"""
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Squared distance from a SparseVector or 1-dimensional NumPy array.
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>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
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>>> a.squared_distance(a)
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0.0
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>>> a.squared_distance(array([1., 2., 3., 4.]))
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11.0
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>>> b = SparseVector(4, [2, 4], [1.0, 2.0])
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>>> a.squared_distance(b)
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30.0
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>>> b.squared_distance(a)
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30.0
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"""
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if type(other) == ndarray:
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if other.ndim == 1:
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result = 0.0
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j = 0 # index into our own array
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for i in xrange(other.shape[0]):
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if j < len(self.indices) and self.indices[j] == i:
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diff = self.values[j] - other[i]
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result += diff * diff
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j += 1
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else:
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result += other[i] * other[i]
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return result
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else:
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raise Exception("Cannot call squared_distance with %d-dimensional array" %
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other.ndim)
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else:
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result = 0.0
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i, j = 0, 0
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while i < len(self.indices) and j < len(other.indices):
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if self.indices[i] == other.indices[j]:
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diff = self.values[i] - other.values[j]
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result += diff * diff
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i += 1
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j += 1
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elif self.indices[i] < other.indices[j]:
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result += self.values[i] * self.values[i]
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i += 1
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else:
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result += other.values[j] * other.values[j]
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j += 1
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while i < len(self.indices):
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result += self.values[i] * self.values[i]
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i += 1
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while j < len(other.indices):
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result += other.values[j] * other.values[j]
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j += 1
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return result
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def toArray(self):
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"""
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Returns a copy of this SparseVector as a 1-dimensional NumPy array.
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"""
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arr = numpy.zeros(self.size)
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for i in xrange(self.indices.size):
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arr[self.indices[i]] = self.values[i]
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return arr
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def __str__(self):
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inds = "[" + ",".join([str(i) for i in self.indices]) + "]"
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vals = "[" + ",".join([str(v) for v in self.values]) + "]"
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return "(" + ",".join((str(self.size), inds, vals)) + ")"
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def __repr__(self):
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inds = self.indices
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vals = self.values
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entries = ", ".join(["{0}: {1}".format(inds[i], vals[i]) for i in xrange(len(inds))])
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return "SparseVector({0}, {{{1}}})".format(self.size, entries)
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def __eq__(self, other):
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"""
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Test SparseVectors for equality.
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>>> v1 = SparseVector(4, [(1, 1.0), (3, 5.5)])
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>>> v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
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>>> v1 == v2
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True
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>>> v1 != v2
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False
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"""
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return (isinstance(other, self.__class__)
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and other.size == self.size
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and array_equal(other.indices, self.indices)
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and array_equal(other.values, self.values))
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def __ne__(self, other):
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return not self.__eq__(other)
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class Vectors(object):
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"""
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Factory methods for working with vectors. Note that dense vectors
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are simply represented as NumPy array objects, so there is no need
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to covert them for use in MLlib. For sparse vectors, the factory
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methods in this class create an MLlib-compatible type, or users
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can pass in SciPy's C{scipy.sparse} column vectors.
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"""
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@staticmethod
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def sparse(size, *args):
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"""
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Create a sparse vector, using either a dictionary, a list of
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(index, value) pairs, or two separate arrays of indices and
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values (sorted by index).
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@param size: Size of the vector.
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@param args: Non-zero entries, as a dictionary, list of tupes,
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or two sorted lists containing indices and values.
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>>> print Vectors.sparse(4, {1: 1.0, 3: 5.5})
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(4,[1,3],[1.0,5.5])
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>>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)])
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(4,[1,3],[1.0,5.5])
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>>> print Vectors.sparse(4, [1, 3], [1.0, 5.5])
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(4,[1,3],[1.0,5.5])
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"""
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return SparseVector(size, *args)
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@staticmethod
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def dense(elements):
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"""
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Create a dense vector of 64-bit floats from a Python list. Always
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returns a NumPy array.
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>>> Vectors.dense([1, 2, 3])
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array([ 1., 2., 3.])
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"""
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return array(elements, dtype=float64)
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@staticmethod
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def stringify(vector):
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"""
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Converts a vector into a string, which can be recognized by
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Vectors.parse().
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>>> Vectors.stringify(Vectors.sparse(2, [1], [1.0]))
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'(2,[1],[1.0])'
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>>> Vectors.stringify(Vectors.dense([0.0, 1.0]))
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'[0.0,1.0]'
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"""
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if type(vector) == SparseVector:
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return str(vector)
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else:
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return "[" + ",".join([str(v) for v in vector]) + "]"
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def _test():
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import doctest
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(failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS)
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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# remove current path from list of search paths to avoid importing mllib.random
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# for C{import random}, which is done in an external dependency of pyspark during doctests.
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import sys
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sys.path.pop(0)
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_test()
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