In [1]:

    

import os
import csv
import platform
import pandas as pd
import networkx as nx
from graph_partitioning import GraphPartitioning, utils

run_metrics = True

cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Qds", "CONDUCTANCE", "MAXPERM", "RBSE", "NMI", "FSCORE", "FSCORE RELABEL IMPROVEMENT", "LONELINESS"]

pwd = %pwd

config = {

    "DATA_FILENAME": os.path.join(pwd, "data", "predition_model_tests", "network", "network_$$.txt"),
    "OUTPUT_DIRECTORY": os.path.join(pwd, "output"),

    # Set which algorithm is run for the PREDICTION MODEL.
    # Either: 'FENNEL' or 'SCOTCH'
    "PREDICTION_MODEL_ALGORITHM": "FENNEL",

    # Alternativly, read input file for prediction model.
    # Set to empty to generate prediction model using algorithm value above.
    "PREDICTION_MODEL": "",

    
    "PARTITIONER_ALGORITHM": "FENNEL",

    # File containing simulated arrivals. This is used in simulating nodes
    # arriving at the shelter. Nodes represented by line number; value of
    # 1 represents a node as arrived; value of 0 represents the node as not
    # arrived or needing a shelter.
    "SIMULATED_ARRIVAL_FILE": os.path.join(pwd,
                                           "data",
                                           "predition_model_tests",
                                           "dataset_6_randomize",
                                           "simulated_arrival_list",
                                           "percentage_of_prediction_randomized_££",
                                           "arrival_££_$$.txt"
                                          ),

    # File containing the prediction of a node arriving. This is different to the
    # simulated arrivals, the values in this file are known before the disaster.
    "PREDICTION_LIST_FILE": os.path.join(pwd,
                                         "data",
                                         "predition_model_tests",
                                         "dataset_6_randomize",
                                         "prediction_list",
                                         "prediction_$$.txt"
                                        ),

    # File containing the geographic location of each node, in "x,y" format.
    "POPULATION_LOCATION_FILE": os.path.join(pwd,
                                             "data",
                                             "predition_model_tests",
                                             "coordinates",
                                             "coordinates_$$.txt"
                                            ),

    # Number of shelters
    "num_partitions": 4,

    # The number of iterations when making prediction model
    "num_iterations": 15,

    # Percentage of prediction model to use before discarding
    # When set to 0, prediction model is discarded, useful for one-shot
    "prediction_model_cut_off": .0,

    # Alpha value used in one-shot (when restream_batches set to 1)
    "one_shot_alpha": 0.5,
    
    "use_one_shot_alpha" : False,
    
    # Number of arrivals to batch before recalculating alpha and restreaming.
    "restream_batches": 50,

    # When the batch size is reached: if set to True, each node is assigned
    # individually as first in first out. If set to False, the entire batch
    # is processed and empty before working on the next batch.
    "sliding_window": False,

    # Create virtual nodes based on prediction model
    "use_virtual_nodes": False,

    # Virtual nodes: edge weight
    "virtual_edge_weight": 1.0,
    
    # Loneliness score parameter. Used when scoring a partition by how many
    # lonely nodes exist.
    "loneliness_score_param": 1.2,

    
    # whether metrics are computed or not
    "compute_metrics_enabled": True,

    ####
    # GRAPH MODIFICATION FUNCTIONS

    # Also enables the edge calculation function.
    "graph_modification_functions": True,

    # If set, the node weight is set to 100 if the node arrives at the shelter,
    # otherwise the node is removed from the graph.
    "alter_arrived_node_weight_to_100": False,

    # Uses generalized additive models from R to generate prediction of nodes not
    # arrived. This sets the node weight on unarrived nodes the the prediction
    # given by a GAM.
    # Needs POPULATION_LOCATION_FILE to be set.
    "alter_node_weight_to_gam_prediction": False,
    
    # Enables edge expansion when graph_modification_functions is set to true
    "edge_expansion_enabled": True,

    # The value of 'k' used in the GAM will be the number of nodes arrived until
    # it reaches this max value.
    "gam_k_value": 100,

    # Alter the edge weight for nodes that haven't arrived. This is a way to
    # de-emphasise the prediction model for the unknown nodes.
    "prediction_model_emphasis": 1.0,
    
    # This applies the prediction_list_file node weights onto the nodes in the graph
    # when the prediction model is being computed and then removes the weights
    # for the cutoff and batch arrival modes
    "apply_prediction_model_weights": True,

    "SCOTCH_LIB_PATH": os.path.join(pwd, "libs/scotch/macOS/libscotch.dylib")
    if 'Darwin' in platform.system()
    else "/usr/local/lib/libscotch.so",
    
    # Path to the PaToH shared library
    "PATOH_LIB_PATH": os.path.join(pwd, "libs/patoh/lib/macOS/libpatoh.dylib")
    if 'Darwin' in platform.system()
    else os.path.join(pwd, "libs/patoh/lib/linux/libpatoh.so"),
    
    "PATOH_ITERATIONS": 5,
        
    # Expansion modes: 'avg_node_weight', 'total_node_weight', 'smallest_node_weight'
    # 'largest_node_weight'
    # add '_squared' or '_sqrt' at the end of any of the above for ^2 or sqrt(weight)
    # i.e. 'avg_node_weight_squared
    "PATOH_HYPEREDGE_EXPANSION_MODE": 'no_expansion',
    
    # Edge Expansion: average, total, minimum, maximum, product, product_squared, sqrt_product
    "EDGE_EXPANSION_MODE" : 'total',
    
    # Whether nodes should be reordered using a centrality metric for optimal node assignments in batch mode
    # This is specific to FENNEL and at the moment Leverage Centrality is used to compute new noder orders
    "FENNEL_NODE_REORDERING_ENABLED": False,
    
    # Whether the Friend of a Friend scoring system is active during FENNEL partitioning.
    # FOAF employs information about a node's friends to determine the best partition when
    # this node arrives at a shelter and no shelter has friends already arrived
    "FENNEL_FRIEND_OF_A_FRIEND_ENABLED": False,
    
    # Alters how much information to print. Keep it at 1 for this notebook.
    # 0 - will print nothing, useful for batch operations.
    # 1 - prints basic information on assignments and operations.
    # 2 - prints more information as it batches arrivals.
    "verbose": 1
}

gp = GraphPartitioning(config)

# Optional: shuffle the order of nodes arriving
# Arrival order should not be shuffled if using GAM to alter node weights
#random.shuffle(gp.arrival_order)

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import scipy
from copy import deepcopy
import time

iterations = 1000


for i in range(0, iterations):
    if (i%20):
        print('Network', str(i))
    conf = deepcopy(config)
    
    conf["DATA_FILENAME"] = conf["DATA_FILENAME"].replace('$$', str(i + 1))
    conf["SIMULATED_ARRIVAL_FILE"] = conf["SIMULATED_ARRIVAL_FILE"].replace('$$', str(i + 1))
    conf["SIMULATED_ARRIVAL_FILE"] = conf["SIMULATED_ARRIVAL_FILE"].replace('££', str(100))

    conf["PREDICTION_LIST_FILE"] = conf["PREDICTION_LIST_FILE"].replace('$$', str(i + 1))
    conf["POPULATION_LOCATION_FILE"] = conf["POPULATION_LOCATION_FILE"].replace('$$', str(i + 1))
    conf["compute_metrics_enabled"] = False

    
    outFileName = os.path.join(pwd, "data", "predition_model_tests", "network", "pm", "network_pm_$$.txt")
    outFileName = outFileName.replace('$$', str(i + 1))

    #with open(outFileName, "r") as inf:
    #    assignments = np.fromiter(inf.readlines(), dtype=np.int32)
    #    print(assignments)
    
    with GraphPartitioning(conf) as gp:
        gp.verbose = 0
        start_time = time.time()
        gp.load_network()
        gp.init_partitioner()
        gp.prediction_model()
        utils.savePredictionFile(outFileName, gp.assignments)
        elapsed_time = time.time() - start_time
        print(elapsed_time)


    

    




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10.439695119857788
Network 956
10.455061197280884
Network 957
10.741245985031128
Network 958
10.686808109283447
Network 959
10.479788064956665
10.426027059555054
Network 961
10.217679023742676
Network 962
10.424928426742554
Network 963
10.543587923049927
Network 964
10.557687044143677
Network 965
10.38128399848938
Network 966
10.532027959823608
Network 967
10.725213050842285
Network 968
10.507293939590454
Network 969
10.608248233795166
Network 970
10.502996444702148
Network 971
10.594212293624878
Network 972
10.469829082489014
Network 973
10.47562289237976
Network 974
10.469002962112427
Network 975
10.384970903396606
Network 976
11.191427946090698
Network 977
10.661219835281372
Network 978
11.040591955184937
Network 979
10.522176027297974
10.65806794166565
Network 981
10.533432006835938
Network 982
15.258654832839966
Network 983
11.122680902481079
Network 984
12.522831916809082
Network 985
11.643926858901978
Network 986
14.099042892456055
Network 987
14.42283010482788
Network 988
10.732295036315918
Network 989
10.977344036102295
Network 990
10.729774236679077
Network 991
10.617187261581421
Network 992
10.567353963851929
Network 993
10.605227947235107
Network 994
10.65161681175232
Network 995
11.056798219680786
Network 996
10.479055643081665
Network 997
12.074808120727539
Network 998
13.744529962539673
Network 999
12.605647802352905

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