spark自带的logistic_regression例子分析

import sys
import numpy as np
from pyspark.sql import SparkSession
D = 10  # Number of dimensions
# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to
# make further computations faster.
# The data file contains lines of the form    ... . We load each block of these
# into a NumPy array of size numLines * (D + 1) and pull out column 0 vs the others in gradient().
def readPointBatch(iterator): 
    strs = list(iterator)
    matrix = np.zeros((len(strs), D + 1))
    for i, s in enumerate(strs):
        matrix[i] = np.fromstring(s.replace(',', ' '), dtype=np.float32, sep=' ')
    return [matrix]

if __name__ == "__main__":

    if len(sys.argv) != 3:
        print("Usage: logistic_regression  ", file=sys.stderr)
        exit(-1)

    print("""WARN: This is a naive implementation of Logistic Regression and is
      given as an example!
      Please refer to examples/src/main/python/ml/logistic_regression_with_elastic_net.py
      to see how ML's implementation is used.""", file=sys.stderr)

    spark = SparkSession\
        .builder\
        .appName("PythonLR")\
        .getOrCreate()

    points = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])\
        .mapPartitions(readPointBatch).cache() 
    iterations = int(sys.argv[2])

    # Initialize w to a random value
    w = 2 * np.random.ranf(size=D) - 1
    print("Initial w: " + str(w))

    # Compute logistic regression gradient for a matrix of data points
    def gradient(matrix, w):
        Y = matrix[:, 0]    # point labels (first column of input file)
        X = matrix[:, 1:]   # point coordinates
        # For each point (x, y), compute gradient function, then sum these up
        return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)

    def add(x, y):
        x += y
        return x

    for i in range(iterations):
        print("On iteration %i" % (i + 1))
        w -= points.map(lambda m: gradient(m, w)).reduce(add)

    print("Final w: " + str(w))

    spark.stop()

1. 其中map()与mapPartitions()函数区别：

map()函数是作用到数据集的每一个元素，所以它的目标是单个的元素

mapPartitions()函数作用到数据集的每个分区上，所以它的目标是迭代器（包含多个元素）

2. enumerate()函数

返回枚举对象

以下展示了使用 enumerate() 方法的实例：

>>>seasons = ['Spring', 'Summer', 'Fall', 'Winter']>>> list(enumerate(seasons))[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]>>> list(enumerate(seasons, start=1)) # 小标从 1 开始[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]

ps:readPointBatch()函数写的挺优雅的

3. numpy fromstring()将字符串转换成一维数组

Examples

>>> np.fromstring('1 2', dtype=int, sep=' ')
array([1, 2])
>>> np.fromstring('1, 2', dtype=int, sep=',')
array([1, 2])