In [4]:

    

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", "NMI", "FSCORE", "FSCORE RELABEL IMPROVEMENT", "LONELINESS"]
#cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Q", "Qds", "CONDUCTANCE", "LONELINESS", "NETWORK PERMANENCE", "NORM. MUTUAL INFO", "EDGE CUT WEIGHT", "FSCORE", "FSCORE RELABEL IMPROVEMENT"]
#cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "MODULARITY", "LONELINESS", "NETWORK PERMANENCE", "NORM. MUTUAL INFO", "EDGE CUT WEIGHT", "FSCORE", "FSCORE RELABEL IMPROVEMENT"]

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_1_shift_rotate",
                                           "simulated_arrival_list",
                                           "percentage_of_prediction_correct_100",
                                           "arrival_100_$$.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_1_shift_rotate",
                                         "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": 10,

    # 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,


    ####
    # 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,
    
    "compute_metrics_enabled": 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 [5]:

    

import scipy
from copy import deepcopy

iterations = 1000 # the number of individual networks to run

FOAF_mode = [False, True]

for mode in FOAF_mode:
#for mode in range(1, 51):

    metricsDataPrediction = []
    metricsDataAssign = []
    
    config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED'] = mode
    
    print('Mode', mode)
    for i in range(0, iterations):
        if (i % 50) == 0:
            print('Mode', mode, 'Iteration', str(i))
        
        conf = deepcopy(config)

        #if mode == 'no_expansion':
        #    config['edge_expansion_enabled'] = False
                    
        #conf["DATA_FILENAME"] =  os.path.join(pwd, "data", "predition_model_tests", "network", "network_" + str(i + 1) + ".txt")
        conf["DATA_FILENAME"] = conf["DATA_FILENAME"].replace('$$', str(i + 1))
        conf["SIMULATED_ARRIVAL_FILE"] = conf["SIMULATED_ARRIVAL_FILE"].replace('$$', str(i + 1))
        conf["PREDICTION_LIST_FILE"] = conf["PREDICTION_LIST_FILE"].replace('$$', str(i + 1))
        conf["POPULATION_LOCATION_FILE"] = conf["POPULATION_LOCATION_FILE"].replace('$$', str(i + 1))


        #print(i, conf)
        #print('config', config)
        
        with GraphPartitioning(conf) as gp:
            #gp = GraphPartitioning(config)
            gp.verbose = 0
            gp.load_network()
            gp.init_partitioner()

            m = gp.prediction_model()
            m = gp.assign_cut_off()
            m = gp.batch_arrival()

            totalM = len(m)
            metricsDataPrediction.append(m[totalM - 1])


    waste = ''
    cutratio = ''
    ec = ''
    tcv = ''
    qds = ''
    conductance = ''
    maxperm = ''
    nmi = ''
    lonliness = ''
    fscore = ''
    fscoreimprove = ''
        
    qdsOv = ''
    condOv = ''

    dataWaste = []
    dataCutRatio = []
    dataEC = []
    dataTCV = [] 
    dataQDS = []
    dataCOND = []
    dataMAXPERM = []
    dataNMI = []
    dataLonliness = []
    dataFscore = []
    dataFscoreImprove = []

    
    for i in range(0, iterations):
        dataWaste.append(metricsDataPrediction[i][0])        
        dataCutRatio.append(metricsDataPrediction[i][1])
        dataEC.append(metricsDataPrediction[i][2])
        dataTCV.append(metricsDataPrediction[i][3])
        dataQDS.append(metricsDataPrediction[i][4])
        dataCOND.append(metricsDataPrediction[i][5])
        dataMAXPERM.append(metricsDataPrediction[i][6])
        dataNMI.append(metricsDataPrediction[i][7])        
        dataFscore.append(metricsDataPrediction[i][8])        
        dataFscoreImprove.append(metricsDataPrediction[i][9])        
        dataLonliness.append(metricsDataPrediction[i][10])


        if(len(waste)):
            waste = waste + ','
        waste = waste + str(metricsDataPrediction[i][0])

        if(len(cutratio)):
            cutratio = cutratio + ','
        cutratio = cutratio + str(metricsDataPrediction[i][1])

        if(len(ec)):
            ec = ec + ','
        ec = ec + str(metricsDataPrediction[i][2])
        
        if(len(tcv)):
            tcv = tcv + ','
        tcv = tcv + str(metricsDataPrediction[i][3])

        if(len(qds)):
            qds = qds + ','
        qds = qds + str(metricsDataPrediction[i][4])

        if(len(conductance)):
            conductance = conductance + ','
        conductance = conductance + str(metricsDataPrediction[i][5])

        if(len(maxperm)):
            maxperm = maxperm + ','
        maxperm = maxperm + str(metricsDataPrediction[i][6])

        if(len(nmi)):
            nmi = nmi + ','
        nmi = nmi + str(metricsDataPrediction[i][7])

        if(len(fscore)):
            fscore = fscore + ','
        fscore = fscore + str(metricsDataPrediction[i][8])

        if(len(fscoreimprove)):
            fscoreimprove = fscoreimprove + ','
        fscoreimprove = fscoreimprove + str(metricsDataPrediction[i][8])
        
        if(len(lonliness)):
            lonliness = lonliness + ','
        lonliness = lonliness + str(dataLonliness[i])
        

    waste = 'WASTE,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataWaste)) + ',' + str(scipy.std(dataWaste)) + ',' + waste

    cutratio = 'CUT_RATIO,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataCutRatio)) + ',' + str(scipy.std(dataCutRatio)) + ',' + cutratio
    ec = 'EC,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataEC)) + ',' + str(scipy.std(dataEC)) + ',' + ec
    tcv = 'TCV,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataTCV)) + ',' + str(scipy.std(dataTCV)) + ',' + tcv

    lonliness = "LONELINESS," + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataLonliness)) + ',' + str(scipy.std(dataLonliness)) + ',' + lonliness
    
    qds = 'QDS,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataQDS)) + ',' + str(scipy.std(dataQDS)) + ',' + qds
    conductance = 'CONDUCTANCE,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataCOND)) + ',' + str(scipy.std(dataCOND)) + ',' + conductance
    maxperm = 'MAXPERM,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataMAXPERM)) + ',' + str(scipy.std(dataMAXPERM)) + ',' + maxperm
    nmi = 'NMI,' + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataNMI)) + ',' + str(scipy.std(dataNMI)) + ',' + nmi

    fscore = "FSCORE," + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataFscore)) + ',' + str(scipy.std(dataFscore)) + ',' + fscore
    fscoreimprove = "FSCORE_IMPROVE," + 'FOAF_mode_' + str(config['FENNEL_FRIEND_OF_A_FRIEND_ENABLED']) + ',' + str(scipy.mean(dataFscoreImprove)) + ',' + str(scipy.std(dataFscoreImprove)) + ',' + fscoreimprove

    print(cutratio)
    print(ec)
    print(tcv)
    print(lonliness)
    print(qds)
    print(conductance)
    print(maxperm)
    print(fscore)
    print(fscoreimprove)


    

    




Mode False
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Mode False Iteration 750
Mode False Iteration 800
Mode False Iteration 850
Mode False Iteration 900
Mode False Iteration 950
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EC,FOAF_mode_False,110.744,21.1287591685,105,115,82,141,132,84,134,145,99,79,123,112,116,141,100,100,100,106,117,103,124,111,110,105,85,136,93,85,130,96,157,86,86,106,99,97,124,102,97,97,144,183,132,93,75,97,124,107,85,72,119,95,124,108,118,87,101,122,121,98,85,108,108,145,184,106,125,157,102,136,126,114,115,87,97,108,113,74,154,88,104,123,126,103,129,126,139,100,96,107,115,109,91,130,120,65,100,67,96,93,63,108,111,96,107,106,97,136,111,64,119,122,151,87,105,106,112,136,131,99,87,112,96,161,93,107,76,92,117,103,89,89,110,137,100,109,134,105,121,105,75,123,82,161,168,131,91,131,175,120,88,90,107,95,90,89,79,105,180,111,107,103,100,119,111,141,81,85,107,73,110,111,127,92,115,123,125,85,126,106,79,123,145,96,121,82,120,113,90,151,104,124,126,129,151,120,113,123,126,159,90,84,104,89,100,103,114,105,97,82,116,117,94,97,123,94,101,123,91,96,104,139,108,114,102,126,112,102,115,172,93,115,91,141,147,103,111,97,93,100,76,96,94,88,83,142,129,150,119,149,109,117,115,117,114,115,127,123,149,105,135,107,106,96,91,90,103,126,112,114,151,106,101,93,127,116,80,117,117,131,117,118,99,111,129,132,150,104,121,144,123,107,93,124,97,130,89,78,105,111,128,111,130,146,127,116,114,87,91,75,126,153,106,135,111,164,85,115,116,121,122,122,88,99,83,123,92,134,98,133,121,101,120,87,92,117,139,134,149,88,135,128,126,118,113,117,122,105,104,111,117,106,115,82,99,111,93,86,114,116,116,93,117,165,95,126,105,148,101,109,116,85,93,78,113,113,105,86,85,102,97,118,126,107,96,131,143,146,107,190,103,82,81,125,100,113,141,113,73,96,113,126,138,86,123,73,106,115,142,123,135,96,120,132,110,137,87,105,122,69,82,123,91,117,107,125,111,104,136,93,117,87,89,109,135,102,113,150,101,95,124,102,110,158,127,149,101,103,111,92,102,97,92,110,79,101,126,90,105,121,99,143,119,115,91,161,104,118,115,93,113,107,122,132,91,137,89,119,86,92,97,133,128,140,112,112,143,98,106,110,121,116,138,98,128,114,131,126,126,111,104,124,97,78,83,108,82,104,77,109,119,92,102,78,112,111,109,70,105,103,121,80,143,97,143,107,100,102,93,135,134,118,100,115,77,104,77,136,87,110,85,104,110,125,115,135,139,88,106,87,110,114,122,113,144,132,103,103,101,86,90,95,101,127,91,97,72,100,61,87,148,141,132,118,95,73,97,125,114,126,106,116,95,80,108,103,161,86,111,122,120,86,103,142,101,118,73,104,87,107,94,113,119,102,153,101,87,116,103,92,93,81,131,95,119,90,119,118,150,69,113,132,80,79,82,139,111,113,87,73,112,135,76,105,125,134,97,113,117,118,131,85,136,125,93,83,142,101,107,55,88,106,151,126,107,121,112,84,79,96,127,119,111,95,107,119,108,102,79,142,110,94,115,135,123,98,119,98,80,131,108,108,134,117,132,70,162,121,102,90,89,114,112,123,136,122,91,134,72,138,117,142,122,101,117,98,103,87,113,101,123,82,90,110,120,117,87,115,127,116,113,118,153,116,104,97,106,117,131,142,78,82,81,116,111,161,124,138,93,113,116,102,97,93,108,92,136,97,121,82,109,108,88,93,142,100,126,123,113,121,97,108,114,139,88,137,116,116,110,103,115,80,94,174,139,86,102,100,121,105,106,95,136,127,108,93,78,139,117,94,94,95,112,171,137,120,77,88,111,79,116,116,141,109,92,82,125,105,98,106,95,92,100,85,103,116,111,136,127,154,103,105,127,92,119,123,104,149,121,144,113,109,116,104,95,147,104,101,89,122,116,113,108,73,116,72,155,73,70,119,123,122,93,103,115,137,172,109,96,105,92,87,89,137,141,136,110,101,136,115,96,130,102,142,95,129,117,106,76,83,110,111,81,114,125,127,105,126,83,130,117,110,117,94,155,118,129,141,129,119,95,97,107,118,114,121,95,75,101,117,112,89,120,120,88,87,111,155,104,169,119,111,100,102,81,81,161,59,103,107,118,113,105,109,119,154,140,102,152,108,87,93,81,124,104,108,99,93,122,103,97,75,105,84,95,159,67,95,73,90,104,110,145,103,120,108,80,138,119,96,93,84,138,93,171,97,99,118,95,111,108,106,97,127,122,161,106,111,107,151,157,177,118,120,99,134,122,113,119,132
TCV,FOAF_mode_False,145.994,22.9691524441,141,162,113,186,175,123,160,190,132,107,148,134,159,185,135,133,136,145,163,141,165,153,148,132,115,193,132,121,163,140,188,125,111,141,130,128,162,134,133,127,185,195,183,128,86,117,160,142,127,101,154,126,158,142,149,114,132,153,144,136,121,160,141,171,214,143,163,211,139,171,152,152,151,123,138,153,156,102,185,125,149,171,145,139,172,167,184,131,135,147,148,147,120,172,155,101,142,92,138,127,85,139,150,138,151,141,130,172,153,97,147,153,198,118,147,141,144,182,164,139,127,151,128,197,123,142,121,125,163,141,130,108,154,164,134,138,161,129,160,142,117,153,109,191,212,167,126,147,203,153,120,123,146,126,127,127,112,139,216,147,135,147,139,161,145,182,118,123,154,104,151,140,161,122,159,157,169,124,156,147,105,155,194,134,150,123,164,150,132,179,145,158,154,165,197,160,139,154,161,199,121,116,133,117,129,146,141,139,124,106,157,149,126,133,159,131,141,151,117,130,137,188,152,160,143,164,156,130,159,211,126,146,121,173,177,126,154,116,134,126,113,132,122,116,118,182,167,183,167,179,148,155,157,149,139,148,163,163,181,147,168,140,147,133,118,125,140,155,157,154,180,151,147,118,162,152,118,143,158,166,161,171,133,150,163,180,202,136,159,189,157,143,132,153,126,161,117,114,148,151,161,137,172,178,167,160,152,126,112,112,165,181,150,180,142,191,126,158,143,159,154,165,116,127,113,173,137,171,133,164,143,145,166,122,126,157,175,177,188,127,169,165,167,147,158,157,134,150,121,158,148,145,160,114,146,139,128,125,149,157,151,128,151,188,122,167,155,170,141,145,136,112,129,116,145,149,149,111,111,140,129,151,180,140,133,180,179,176,136,226,139,111,111,163,130,145,174,154,105,124,150,153,160,121,167,104,135,158,180,152,168,137,152,166,147,170,125,153,156,98,111,163,126,157,142,162,149,140,171,116,160,123,127,155,175,142,137,176,138,127,172,143,142,191,147,179,132,144,156,129,128,138,122,144,118,138,176,115,131,159,137,160,144,154,136,191,131,135,163,132,154,137,161,166,128,172,124,149,114,133,121,184,179,176,146,153,185,131,139,147,164,154,178,126,175,145,167,152,152,146,140,157,128,111,117,143,122,135,107,144,164,127,134,112,152,148,151,104,124,138,157,103,178,127,174,147,132,142,135,168,168,149,139,150,108,139,101,162,122,144,123,133,157,153,146,172,164,120,140,117,151,152,176,138,184,177,141,135,125,119,116,135,137,166,126,123,94,126,89,124,181,178,162,162,137,110,136,171,155,158,141,149,127,122,143,144,192,122,157,154,158,113,130,174,146,147,111,135,119,147,139,146,155,137,194,145,122,147,143,122,119,120,169,128,164,130,154,149,199,101,153,165,111,110,110,170,139,148,115,103,146,170,117,141,160,161,135,158,152,158,178,121,186,165,136,113,171,126,134,80,128,139,183,156,155,158,153,117,106,131,164,152,145,135,137,164,146,146,110,170,141,131,146,164,148,143,159,123,121,168,136,140,161,163,172,102,184,150,134,133,121,151,146,152,183,169,125,163,114,179,143,167,158,129,148,133,140,123,141,147,174,111,127,139,142,149,114,147,167,157,145,165,192,159,136,126,144,153,179,192,103,115,120,151,140,176,165,182,121,145,151,145,125,126,134,115,170,125,154,116,134,136,124,130,173,127,163,151,158,160,129,145,131,176,126,188,170,138,148,129,150,112,139,211,180,115,135,136,163,139,137,135,177,178,142,126,117,175,154,122,137,131,147,199,166,152,119,119,147,116,143,159,190,152,130,112,165,150,130,136,123,119,137,121,129,150,150,160,166,182,135,143,171,124,151,163,133,188,152,192,161,141,147,144,125,184,140,138,124,162,152,150,164,106,154,109,197,106,107,149,160,150,127,144,139,174,192,141,124,141,139,107,128,166,179,182,152,140,169,131,124,173,132,177,122,182,145,142,106,108,152,150,109,145,154,162,144,162,123,167,161,147,167,137,192,143,166,175,150,151,129,128,144,141,154,157,125,110,141,166,156,123,156,163,117,106,148,186,143,193,164,156,140,134,121,116,202,87,134,143,152,151,138,143,161,182,170,139,178,145,122,127,107,161,135,140,140,141,146,146,137,106,137,118,132,196,101,130,106,128,134,159,172,157,154,143,110,176,161,132,119,111,182,131,217,130,133,157,122,150,144,135,133,153,163,203,147,138,139,191,202,216,146,168,130,176,163,154,152,161
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Mode True Iteration 900
Mode True Iteration 950
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EC,FOAF_mode_True,72.62,17.94635339,90,91,77,118,116,44,107,91,78,36,76,81,72,81,76,75,58,78,99,80,60,64,64,58,67,98,57,61,63,62,95,56,95,73,90,60,54,69,53,75,77,117,78,61,55,52,42,88,55,64,73,57,87,78,86,63,62,72,82,64,73,62,80,86,135,83,103,98,72,71,90,76,54,47,72,98,70,56,70,55,66,79,96,75,78,60,106,83,90,75,63,68,53,88,89,38,59,51,48,46,49,76,75,66,77,70,70,106,74,47,91,86,72,47,73,54,87,97,94,61,47,91,68,90,63,79,58,65,90,63,78,66,43,96,73,63,100,57,74,59,48,65,66,85,118,76,66,100,83,85,59,52,74,52,51,63,68,83,126,84,52,85,50,102,79,84,68,59,65,75,75,74,74,46,75,80,61,77,110,75,49,90,79,49,85,56,59,94,61,118,50,109,51,64,91,79,69,78,65,109,67,55,72,55,70,72,78,77,61,67,82,94,68,62,60,64,62,92,48,52,82,98,61,56,63,78,52,67,74,100,44,57,76,122,76,61,91,45,73,57,49,58,46,87,31,79,82,125,80,71,63,86,76,49,54,46,75,92,88,85,83,64,65,74,61,60,55,61,80,77,104,76,70,55,80,76,61,63,76,104,100,96,69,73,90,75,74,63,84,81,88,69,49,84,60,83,53,72,54,71,107,86,100,107,67,53,80,49,44,62,94,84,80,90,58,100,51,66,69,78,99,72,68,77,40,64,57,73,51,94,52,63,79,52,52,68,76,92,99,63,83,84,89,82,70,113,64,81,72,84,89,37,95,47,66,94,78,53,69,85,100,67,47,97,46,60,81,104,41,88,67,53,70,49,91,59,70,54,57,78,89,75,60,67,73,67,90,95,70,137,56,73,47,85,70,62,99,70,53,70,65,72,100,59,74,60,49,74,81,101,80,54,71,83,87,101,59,77,84,26,53,80,36,77,62,79,58,40,83,52,69,74,40,92,95,49,79,103,43,45,72,64,93,86,77,99,94,51,73,57,83,76,55,70,48,61,65,60,84,99,80,65,58,78,56,117,84,82,80,54,48,59,88,73,52,98,59,95,58,56,56,78,90,119,100,61,99,60,74,56,83,82,78,61,69,84,79,110,96,97,84,54,51,62,65,66,70,72,62,71,78,62,95,58,75,58,41,37,76,78,54,65,69,51,85,75,71,77,61,76,83,83,63,91,51,55,23,88,56,68,52,79,108,94,89,96,91,62,90,77,54,63,65,95,90,94,46,50,47,56,39,64,72,80,50,63,50,63,46,54,89,93,86,81,74,71,49,78,68,93,57,81,61,58,74,63,129,51,64,77,88,71,81,105,59,79,39,82,68,91,65,94,68,73,99,50,54,51,68,52,82,65,77,36,70,59,83,72,108,46,104,76,70,40,45,84,62,89,32,59,90,80,55,76,97,82,62,68,75,69,94,69,97,84,62,44,128,42,69,50,75,100,75,64,65,81,64,61,53,70,115,62,54,91,77,76,64,64,50,119,71,91,78,100,97,48,88,82,45,104,71,83,112,61,79,43,129,83,54,56,65,76,77,84,87,75,68,56,40,78,78,119,68,59,76,63,57,66,67,66,61,41,49,76,90,69,53,57,72,85,68,75,131,91,54,48,65,86,91,91,78,52,41,66,56,126,67,99,63,50,95,71,73,70,84,86,69,69,88,64,42,98,52,42,96,85,74,97,55,89,78,80,83,96,61,113,64,64,88,41,76,54,75,122,87,66,84,77,73,69,55,68,90,71,74,69,67,90,65,41,78,63,77,94,84,70,47,59,80,60,85,77,79,68,49,62,89,67,66,72,69,47,54,56,63,65,45,87,94,95,81,70,80,58,59,100,55,83,100,86,73,79,78,76,45,86,86,72,68,103,86,64,38,65,56,71,104,57,53,96,79,67,60,75,89,62,96,62,58,87,42,45,59,109,59,75,106,67,78,82,82,77,70,87,56,78,60,66,60,53,63,88,51,71,79,103,57,86,49,89,100,82,78,65,101,60,108,81,107,86,69,54,66,89,63,89,63,53,62,63,84,46,94,62,47,52,64,77,83,77,86,86,71,57,59,62,95,56,74,65,81,61,80,47,92,80,106,58,66,72,62,67,46,58,62,67,70,59,67,88,53,56,69,66,67,90,45,75,49,70,78,70,76,63,66,72,50,86,57,60,60,66,69,80,87,75,73,67,57,76,47,95,52,69,76,114,78,61,77,110,90,121,102,82,67,79,99,54,85,70
TCV,FOAF_mode_True,95.49,19.5018947797,117,124,95,138,133,68,123,117,106,53,91,102,98,108,91,95,86,113,127,104,75,95,88,82,94,127,89,79,84,90,119,83,116,99,113,86,83,94,72,90,104,135,108,84,62,74,60,109,82,86,96,81,112,106,112,79,75,103,86,89,98,91,100,106,149,110,134,134,92,85,108,114,79,66,101,138,86,85,96,77,88,105,109,100,112,80,135,102,117,104,81,97,70,116,117,59,79,61,68,72,70,98,97,102,106,89,91,127,104,62,116,104,96,69,103,74,103,124,124,87,71,119,84,125,84,100,90,86,114,90,102,81,64,109,92,85,114,76,96,88,72,83,90,103,136,107,90,111,104,103,81,72,97,74,76,77,97,105,142,104,72,105,71,136,92,108,87,87,87,103,100,97,95,67,100,106,91,119,115,100,65,113,117,68,101,75,83,116,87,142,72,120,71,89,115,109,88,92,81,140,98,78,99,73,91,103,100,106,77,83,110,123,94,85,87,89,93,110,61,70,95,120,89,83,84,102,80,83,100,125,59,75,95,148,93,77,115,58,97,83,69,78,67,102,49,102,105,156,108,100,88,109,95,72,65,63,86,119,108,112,113,84,89,100,91,81,86,82,110,94,134,112,103,79,95,105,95,84,105,125,119,126,90,103,121,105,112,75,114,104,103,97,70,120,80,112,62,100,76,96,140,107,123,138,94,77,105,74,56,83,117,109,114,116,72,114,78,96,84,103,124,96,90,94,59,91,83,91,74,120,55,96,108,82,67,97,100,112,123,85,108,111,110,111,103,134,73,106,87,104,114,59,126,73,93,108,99,82,98,114,127,86,65,111,63,90,107,122,58,114,81,73,93,77,107,78,90,75,77,106,108,93,89,83,93,89,110,120,86,167,78,85,63,109,101,92,128,101,77,85,88,90,117,86,100,81,72,105,95,124,104,73,87,111,114,127,72,111,108,40,73,98,48,99,87,110,79,63,107,71,98,104,64,128,120,73,97,112,64,62,100,83,121,101,92,115,119,73,95,81,107,99,71,88,73,77,97,79,106,121,109,95,77,99,82,131,109,100,120,74,70,75,115,100,71,116,77,121,83,82,86,107,114,148,123,93,122,79,97,86,114,106,107,82,82,97,95,128,109,129,111,76,74,86,91,79,97,98,79,97,102,83,117,77,96,82,58,60,91,100,74,78,93,74,101,101,91,102,91,97,108,108,86,112,66,79,31,103,74,100,79,96,123,120,111,120,106,90,112,100,83,88,95,116,109,120,73,72,66,80,53,94,90,112,64,90,63,83,68,78,111,116,108,118,98,92,69,104,88,117,80,94,87,84,95,91,156,64,90,94,110,93,103,128,87,106,62,103,89,115,91,114,87,93,132,69,79,70,90,77,99,94,97,56,94,83,110,89,146,72,113,97,81,64,65,104,76,108,46,83,122,103,77,96,117,95,88,90,96,95,118,87,132,110,87,59,145,59,92,68,104,116,84,92,86,99,95,81,75,98,133,85,73,115,100,103,91,98,66,142,86,126,98,117,128,75,112,97,66,117,90,109,139,88,107,62,150,104,73,82,84,102,100,110,111,108,89,78,65,102,93,136,80,88,98,75,74,88,93,92,87,61,70,101,116,88,74,82,98,111,88,102,162,129,77,64,84,107,122,126,96,72,67,86,80,136,95,131,85,65,121,99,98,94,99,113,94,82,119,84,65,127,78,62,115,105,109,113,80,106,102,110,90,114,82,141,97,77,116,57,100,78,100,150,111,88,106,101,106,85,80,83,118,98,103,94,86,116,86,60,114,83,106,119,98,93,71,83,107,83,93,110,111,96,71,84,106,97,90,92,94,66,77,80,79,87,61,102,113,110,110,93,104,83,82,120,80,108,130,110,96,92,94,108,60,109,112,102,91,122,109,92,65,89,82,93,138,83,84,117,99,88,77,103,97,78,112,84,72,119,68,59,85,126,78,99,140,95,101,101,107,98,95,122,74,112,76,93,82,72,92,113,80,89,102,121,81,100,75,111,134,108,107,95,117,70,132,98,129,106,94,76,86,105,89,116,80,73,87,82,105,70,115,80,67,68,91,91,113,94,115,115,97,78,81,81,120,74,99,92,100,87,104,67,130,101,131,86,85,97,87,80,66,90,79,85,92,79,88,120,78,82,91,95,100,108,63,92,73,98,104,104,106,104,90,93,67,113,80,88,82,84,94,108,113,103,97,90,81,101,63,115,76,87,96,139,102,77,88,134,111,146,124,111,90,102,125,81,115,84
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