#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
This example requires numpy (http://www.numpy.org/)
"""
from collections import namedtuple
from math import exp
from os.path import realpath
import sys

import numpy as np
from pyspark import SparkContext


N = 100000  # Number of data points
D = 10  # Number of dimensions
R = 0.7   # Scaling factor
ITERATIONS = 5
np.random.seed(42)


DataPoint = namedtuple("DataPoint", ['x', 'y'])
from logistic_regression import DataPoint  # So that DataPoint is properly serialized


def generateData():
    def generatePoint(i):
        y = -1 if i % 2 == 0 else 1
        x = np.random.normal(size=D) + (y * R)
        return DataPoint(x, y)
    return [generatePoint(i) for i in range(N)]


if __name__ == "__main__":
    if len(sys.argv) == 1:
        print >> sys.stderr, "Usage: logistic_regression <master> [<slices>]"
        exit(-1)
    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
    points = sc.parallelize(generateData(), slices).cache()

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

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

    for i in range(1, ITERATIONS + 1):
        print "On iteration %i" % i

        gradient = points.map(lambda p:
            (1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x
        ).reduce(add)
        w -= gradient

    print "Final w: " + str(w)

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
A logistic regression implementation that uses NumPy (http://www.numpy.org) to act on batches
of input data using efficient matrix operations.
"""
from collections import namedtuple
from math import exp
from os.path import realpath
import sys

import numpy as np
from pyspark import SparkContext


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 <label> <x1> <x2> ... <xD>. 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 in xrange(len(strs)):
        matrix[i] = np.fromstring(strs[i], dtype=np.float32, sep = ' ')
    return [matrix]

if __name__ == "__main__":
    if len(sys.argv) != 4:
        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
        exit(-1)
    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
    iterations = int(sys.argv[3])

    # 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)

Update the Python logistic regression example to read from a file and batch input records for more efficient NumPy computations