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", "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": 1,

    # 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": 20,

    # 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,

    "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

iterations = 1000 # the number of individual networks to run

alpha_mode = [False, True]

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

    metricsDataPrediction = []
    metricsDataAssign = []
    
    config['use_one_shot_alpha'] = 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,' + 'alpha_mode_' + str(config['num_iterations']) + ',' + str(scipy.mean(dataWaste)) + ',' + str(scipy.std(dataWaste)) + ',' + waste

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

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

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


    

    




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warning: graph_partitioning/fennel.pyx:244:60: Index should be typed for more efficient access






    




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Mode False Iteration 950
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EC,alpha_mode_1,124.86,22.9312537817,161,155,114,128,149,104,154,130,101,114,138,146,130,138,120,126,151,118,155,146,113,87,122,109,110,180,95,132,142,98,159,132,118,132,113,111,149,124,92,78,141,196,148,135,54,105,157,119,92,111,109,131,119,119,144,108,111,161,119,162,132,122,135,164,182,148,122,133,150,168,126,131,122,140,111,147,103,91,146,126,96,116,132,96,136,135,148,102,95,92,123,138,127,133,138,71,104,94,109,108,104,117,143,99,119,110,92,108,115,79,125,126,135,102,115,116,142,134,132,115,94,132,91,145,135,142,93,91,116,125,95,114,138,163,152,106,145,154,122,120,74,131,98,146,138,145,86,119,153,125,100,107,117,136,109,104,102,124,213,153,133,125,125,156,141,147,99,103,128,119,137,125,129,135,139,139,142,104,147,126,86,114,141,100,112,97,127,166,93,157,132,136,129,106,140,127,97,151,138,152,92,115,85,102,96,100,142,101,81,87,129,123,113,111,140,111,120,164,98,106,141,150,120,142,108,133,150,128,101,166,109,133,127,172,136,111,136,104,82,118,84,115,112,123,107,135,129,131,118,162,105,135,122,131,136,111,101,112,130,88,165,132,118,117,115,100,113,103,132,122,143,123,94,113,130,119,108,153,139,122,160,139,111,146,149,119,150,99,175,119,132,148,97,144,116,126,112,121,135,108,155,142,117,157,119,135,106,82,109,111,161,181,114,136,118,165,91,146,100,151,154,136,111,134,110,156,93,158,119,148,142,98,138,107,121,125,164,136,187,118,113,140,140,148,111,151,115,142,139,110,138,126,110,119,114,128,117,102,116,126,142,116,112,197,105,153,98,134,127,124,106,77,117,125,117,136,135,97,79,120,111,153,116,102,102,155,147,146,142,188,145,116,90,143,109,120,141,128,103,145,111,109,161,79,128,111,115,125,143,119,159,95,105,125,132,157,94,140,125,90,115,124,103,150,114,140,134,108,148,127,121,95,95,135,151,107,122,209,128,105,118,112,145,156,135,151,124,106,132,115,116,103,99,107,100,118,144,90,147,137,108,158,124,120,134,168,128,167,128,113,109,150,148,145,117,157,101,153,100,122,128,149,142,146,122,111,150,128,131,124,130,144,113,107,121,119,127,116,133,135,131,135,103,95,111,111,114,113,102,116,126,121,101,95,123,143,121,113,101,111,93,115,122,131,153,105,115,128,92,148,156,136,116,119,104,103,104,146,100,110,92,143,147,135,105,143,146,110,158,85,130,140,118,141,143,153,124,99,113,92,125,125,104,155,106,117,124,129,108,89,132,134,147,139,103,94,112,135,115,121,132,131,134,116,134,129,159,93,130,154,143,102,126,150,119,111,82,171,116,160,93,134,124,110,132,115,101,120,124,126,109,99,118,109,123,117,123,123,137,117,115,162,105,98,99,143,104,106,102,88,128,128,123,102,135,135,144,141,95,114,152,128,148,125,89,95,160,108,101,66,75,134,144,145,114,134,98,156,101,126,151,128,107,113,124,125,112,116,120,153,117,142,111,136,123,113,114,151,110,124,114,141,147,141,159,90,179,157,135,101,97,132,125,134,149,137,108,125,90,124,136,174,150,122,122,138,113,130,143,103,119,106,131,115,108,183,79,142,137,132,128,154,181,125,115,122,110,116,130,172,111,104,117,111,122,215,116,139,120,158,140,125,111,102,100,104,124,109,114,109,121,118,113,115,128,162,152,133,112,159,123,129,157,156,110,156,160,103,125,96,114,93,79,178,133,109,130,114,118,79,108,117,166,139,154,121,89,154,175,114,117,134,111,181,174,112,93,106,132,87,157,129,145,108,98,110,153,120,138,111,115,96,92,96,131,102,156,143,144,165,92,140,126,118,141,140,122,111,158,144,140,112,134,133,76,168,111,104,130,149,121,95,99,95,151,84,150,113,75,82,137,157,100,141,133,155,168,116,116,126,106,114,98,178,121,176,120,140,124,137,72,135,102,172,131,107,121,125,73,105,104,138,94,168,180,175,114,156,104,105,112,122,113,97,184,139,130,177,180,151,115,128,122,122,132,163,112,92,103,140,111,108,145,137,111,143,125,133,127,172,159,116,87,133,118,88,169,89,89,139,122,143,114,143,128,151,155,126,139,101,104,126,125,134,124,109,109,92,144,126,97,84,101,137,88,153,73,109,91,111,136,145,115,124,143,141,103,140,155,112,82,79,138,156,163,112,91,122,109,98,128,125,100,101,128,144,126,114,143,156,138,182,120,133,129,162,144,125,119,139
TCV,alpha_mode_1,160.947,24.1947141128,199,200,161,172,202,144,187,172,141,146,166,173,156,167,142,160,194,164,199,197,148,120,164,142,147,219,130,174,183,138,204,178,153,158,166,144,179,170,126,101,177,228,183,181,80,134,179,151,130,133,145,161,158,148,184,132,148,184,148,199,175,178,160,185,208,190,153,185,178,210,156,170,161,165,148,194,142,112,181,169,133,152,149,137,174,165,186,133,135,139,154,171,161,175,185,99,132,120,155,147,134,164,177,147,162,147,128,150,160,113,164,149,176,136,161,156,182,179,170,156,130,164,117,195,174,180,129,129,158,173,138,130,179,203,199,138,183,182,159,153,106,170,131,178,184,179,122,128,189,166,117,141,141,164,145,141,144,150,235,189,174,157,163,192,181,180,147,142,171,153,172,161,168,167,183,168,179,133,164,151,122,143,186,140,133,140,167,181,135,198,161,165,157,138,193,162,130,179,178,201,125,149,109,133,134,139,192,128,113,110,154,168,151,143,174,154,173,191,120,142,187,193,159,187,153,154,181,156,130,212,131,159,158,203,174,139,189,123,114,148,117,155,139,172,144,175,166,169,148,179,143,177,161,167,180,150,139,148,165,122,205,162,154,151,150,137,148,136,162,176,183,175,136,141,174,157,150,181,181,156,211,181,144,168,196,156,196,135,220,162,170,183,135,179,152,158,146,167,180,146,186,164,166,207,153,166,143,115,136,147,199,217,165,178,152,192,127,194,132,187,185,177,151,161,150,198,129,208,161,174,165,134,192,149,150,167,198,178,228,163,153,178,180,196,154,188,130,196,169,154,170,171,148,157,160,160,154,134,159,182,170,157,157,221,135,195,134,159,177,156,128,102,150,169,149,169,179,129,122,144,144,189,155,130,148,183,177,182,167,228,177,153,121,173,147,153,188,171,122,182,138,134,185,123,171,157,150,169,174,155,192,129,146,174,172,195,125,190,151,109,145,168,133,186,146,184,182,137,186,162,162,123,131,171,179,146,147,224,157,131,163,151,190,172,141,185,167,140,182,155,146,142,129,137,137,164,194,119,168,187,140,181,158,159,155,208,174,195,170,153,143,179,194,194,152,196,136,178,124,164,163,188,182,186,166,149,196,165,165,170,169,189,156,150,161,160,167,143,161,171,163,178,134,123,153,151,152,146,138,148,161,162,132,123,154,181,160,147,132,155,131,152,152,169,196,149,147,169,128,187,189,154,152,153,146,145,130,182,128,158,129,172,185,175,142,187,172,137,195,122,174,183,168,168,189,189,158,134,142,128,157,172,139,197,141,154,161,163,145,120,172,170,178,173,142,137,146,174,144,155,169,151,171,155,169,165,173,126,171,188,179,138,153,191,171,142,120,200,145,202,135,178,161,145,177,159,134,159,156,160,141,129,155,135,162,150,161,151,193,157,150,192,136,133,128,187,131,152,138,131,152,160,167,136,175,171,190,182,136,152,199,162,196,162,128,131,197,142,139,94,118,168,167,179,153,161,130,196,134,164,184,165,140,154,164,148,152,168,151,187,155,187,158,173,154,150,157,176,150,154,148,188,176,180,204,125,193,195,185,147,131,177,155,173,187,177,150,152,136,161,165,199,182,148,160,159,147,172,166,145,161,139,162,153,143,217,118,174,181,166,161,194,219,168,148,148,150,154,170,218,144,134,150,149,152,230,142,190,157,193,164,163,155,144,133,140,152,148,147,148,159,151,144,154,164,195,202,168,167,205,171,171,179,198,146,205,210,131,156,137,146,128,119,206,163,154,155,144,155,111,145,165,208,171,196,148,126,183,217,156,160,185,156,226,205,156,138,140,180,122,175,168,180,152,141,146,191,168,168,148,159,124,133,136,174,136,184,175,178,187,123,176,183,150,159,172,163,149,176,181,177,141,157,188,114,193,157,138,168,183,149,128,141,123,173,115,206,150,110,107,176,177,127,174,163,183,200,149,151,168,150,136,133,218,157,232,169,184,157,165,98,183,125,193,171,148,156,168,102,143,143,173,131,196,213,204,150,193,149,145,153,154,153,124,226,160,176,209,207,190,154,160,150,160,180,195,148,131,138,179,155,146,173,191,155,164,159,164,163,200,201,162,129,174,161,118,199,124,122,188,170,164,147,180,175,173,194,167,163,137,137,162,158,186,156,141,152,125,170,159,136,122,138,189,117,188,106,151,134,142,178,181,153,182,164,174,130,183,185,152,110,107,179,190,201,145,119,156,147,135,171,159,142,131,161,182,165,148,171,194,185,218,147,164,166,212,183,171,152,168
LONELINESS,alpha_mode_1,0.747051366128,0.0164157690574,0.742250079098,0.754501257554,0.739816191192,0.755351167164,0.741968482278,0.72816101316,0.774917744041,0.749007711113,0.751146261461,0.738516257982,0.76109639969,0.750349472129,0.729573733,0.767043526642,0.760046512599,0.733532586475,0.73511592438,0.75439536476,0.766937757847,0.73822851577,0.755787023615,0.738830066824,0.684189318803,0.747182753289,0.743136624509,0.764912004189,0.745575594952,0.733472700564,0.720382740613,0.721762759071,0.741677093842,0.774415713655,0.738934713959,0.746699988362,0.765864602613,0.748338026035,0.75952342322,0.74464386995,0.742782955428,0.771732596399,0.752704409674,0.747353797559,0.741557677091,0.741269579114,0.739475946761,0.750115906173,0.755306625563,0.735356914772,0.715969258671,0.753041403105,0.758705214531,0.732516424759,0.747666863548,0.75422180152,0.756145170743,0.733905601846,0.747531087448,0.755551212476,0.749476542442,0.734509888277,0.720147137282,0.744670930336,0.746249075718,0.751933955393,0.774367519199,0.756062702746,0.761103165169,0.731229125119,0.744754312301,0.761123563799,0.772278962877,0.767340765466,0.764363587257,0.732298237346,0.762327746946,0.765776502418,0.735826556248,0.752128490661,0.762313260533,0.742384059321,0.73452427322,0.755069126346,0.738511811349,0.750473318255,0.749510953476,0.759905542606,0.763433746052,0.730632582457,0.722353217079,0.729595844965,0.750064850507,0.759491884426,0.748741841219,0.745730169829,0.745080914555,0.745249061179,0.747393032432,0.742296767318,0.751712119933,0.713421096609,0.746327180873,0.754430520296,0.757114376977,0.766897542455,0.728707331045,0.745803417506,0.734788912624,0.729095628965,0.754488287362,0.719770053191,0.761116051823,0.787565428299,0.764030807814,0.746395486749,0.731171862924,0.754799102301,0.730385242219,0.779737406307,0.760585602704,0.757109040152,0.744483236648,0.73952132225,0.772772967817,0.760495286871,0.733556905404,0.761366811979,0.72401937233,0.767844329353,0.746610500075,0.725436460454,0.717511419958,0.749542000068,0.726238544067,0.759091529304,0.74329931543,0.747557830226,0.751628635679,0.751755441621,0.761454301471,0.758232609671,0.764466429539,0.740579806202,0.740093135409,0.738628903598,0.743373184472,0.763715078729,0.751094065049,0.765026347789,0.774550219787,0.767749014534,0.767234438354,0.755508993346,0.74926074532,0.752073044411,0.740373646184,0.732053386394,0.752100994173,0.734623857675,0.744422903106,0.756732706835,0.748563471976,0.757800234194,0.747011199042,0.756317553779,0.751843587366,0.749651424008,0.712775943283,0.734185362109,0.731951618446,0.727775900452,0.745394671925,0.755736794701,0.747090735021,0.741404449338,0.752127748523,0.758203544971,0.749518942471,0.756502923584,0.749078518303,0.737907145119,0.715199159325,0.787597929213,0.728995934347,0.693490013467,0.765698233769,0.74770588753,0.731901653571,0.754155216985,0.748748709942,0.752704389588,0.741339570816,0.745483078582,0.746555107971,0.769778551731,0.742785425213,0.761324879665,0.746869425989,0.732536540409,0.748170814703,0.757729601666,0.738061904843,0.716481143825,0.741869970127,0.739242576788,0.743394026671,0.762650026888,0.722823583709,0.730266692086,0.747501679885,0.757698691028,0.754096764658,0.76281896737,0.737803077471,0.746695064037,0.74267963294,0.708442474595,0.714827285732,0.755723684188,0.720831169862,0.733037587031,0.733878904052,0.757793249396,0.758645254476,0.742391878284,0.733950027196,0.753057702977,0.735193828417,0.714538259447,0.77593746685,0.772218854773,0.742373755641,0.761137569642,0.742470493006,0.756493145695,0.773139138494,0.748389518287,0.750672253888,0.723322550158,0.772202441153,0.76368541004,0.741769568129,0.70858136322,0.730684086473,0.742250878184,0.743841400995,0.76318282668,0.74650370133,0.770909175062,0.774774021312,0.768102356331,0.746238799389,0.728603553757,0.744621108562,0.734718664404,0.759254877731,0.758184593204,0.761525809566,0.763696998409,0.75627393303,0.745713150492,0.769821638881,0.729276021293,0.73674603637,0.776293461575,0.749002615441,0.717632912377,0.754316909025,0.752148190954,0.742944542468,0.71991147179,0.767203431656,0.765472078393,0.764220688791,0.763632539389,0.735736436044,0.734415991782,0.755041520504,0.754801058293,0.741706463404,0.732577828045,0.742512573536,0.769320851742,0.723439203343,0.770810405159,0.741347129088,0.76403728729,0.779960958809,0.734676828009,0.74845346822,0.778970813182,0.748638716733,0.755235571584,0.765848211392,0.762603895058,0.716354786702,0.761844434006,0.741050238365,0.742633723041,0.759404448491,0.769882617277,0.771474687213,0.764872568725,0.763560540163,0.76338565779,0.754908673015,0.752344779059,0.760801739672,0.734142164678,0.752123961228,0.732993072345,0.759640803134,0.765812189785,0.751888434204,0.733826197219,0.720885225788,0.775498559292,0.724011153817,0.750771540772,0.759596626819,0.76849985618,0.737087505691,0.738164760352,0.743514007985,0.768564036571,0.73936477559,0.716866408101,0.730481877886,0.743808412685,0.730423810789,0.776062200586,0.750744615456,0.7573000203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Mode True
Mode True Iteration 0
Mode True Iteration 50
Mode True Iteration 100
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Mode True Iteration 600
Mode True Iteration 650
Mode True Iteration 700
Mode True Iteration 750
Mode True Iteration 800
Mode True Iteration 850
Mode True Iteration 900
Mode True Iteration 950
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EC,alpha_mode_1,133.271,24.210980133,166,158,122,160,165,105,159,133,111,128,153,168,135,147,127,131,153,132,162,134,123,82,131,120,110,187,107,139,147,103,167,140,121,147,114,112,150,127,104,87,141,228,146,160,55,108,169,123,103,114,123,120,119,130,153,111,113,163,162,170,132,126,140,170,173,161,126,137,152,167,177,137,141,145,117,153,105,92,183,129,100,131,145,104,146,144,160,108,93,100,156,142,107,144,141,83,106,117,121,126,116,140,149,100,128,116,106,122,121,85,134,144,158,106,127,129,144,147,138,121,100,134,99,172,128,157,98,106,135,124,104,122,151,175,155,112,159,147,134,109,67,130,110,147,149,150,86,122,164,130,102,115,129,139,122,109,108,129,219,161,147,133,134,161,158,151,107,109,150,123,148,136,128,141,145,144,143,108,155,142,87,115,159,99,145,97,132,164,99,163,132,144,130,111,146,138,101,162,136,160,100,117,92,108,101,111,146,104,94,94,135,126,111,119,164,111,123,176,109,118,144,153,136,149,113,140,151,130,119,176,109,158,131,172,145,116,147,126,95,134,90,116,119,123,111,158,131,142,132,178,121,161,128,118,147,111,117,142,141,117,165,141,121,159,120,118,123,97,142,126,170,136,102,118,133,133,112,164,150,136,167,148,115,169,165,136,162,105,192,133,128,149,109,155,115,138,123,124,153,114,168,155,138,162,132,143,135,83,117,122,163,187,120,144,128,173,104,132,102,157,166,159,108,131,105,154,96,162,122,135,145,100,143,119,123,133,174,139,201,120,118,141,146,146,106,166,134,142,141,114,138,143,113,129,127,130,138,119,122,137,153,117,124,202,110,163,99,145,129,126,106,86,135,123,123,138,143,114,70,150,116,157,118,115,105,167,147,165,155,193,146,130,90,159,117,124,154,130,110,149,124,115,163,85,130,119,120,136,143,126,164,117,120,127,159,173,97,134,137,98,104,128,110,158,135,153,144,121,150,143,124,101,99,164,154,110,127,211,130,112,124,113,161,157,134,170,128,110,135,116,123,103,112,111,106,140,168,97,169,141,135,170,134,133,133,173,147,168,135,112,111,153,167,147,116,161,106,160,105,129,137,163,149,140,127,112,172,127,131,130,143,148,128,113,130,126,133,125,151,130,144,141,115,110,134,117,119,117,114,108,161,124,103,103,136,148,121,114,111,116,97,123,127,136,157,103,119,140,112,160,162,140,116,152,107,107,104,151,105,122,92,152,147,144,108,148,168,121,158,118,145,149,132,151,155,162,133,117,128,98,139,133,124,166,112,123,123,141,116,103,165,136,146,162,111,92,115,153,119,130,133,135,143,113,133,146,184,107,130,152,151,108,148,162,124,137,82,172,123,161,104,135,134,111,144,115,130,134,142,144,120,120,124,109,153,123,130,135,163,121,128,147,113,109,122,150,119,123,103,98,134,148,129,108,142,143,147,141,110,128,177,138,157,161,97,96,176,113,126,75,95,139,155,147,111,130,125,168,120,127,156,133,116,139,126,169,111,124,120,159,132,136,133,151,138,111,120,163,109,149,122,155,151,160,157,91,165,178,132,116,99,142,147,166,177,152,114,128,94,131,135,194,154,133,126,141,118,141,145,110,121,112,136,152,113,196,89,135,160,132,150,162,194,143,117,127,113,125,132,165,140,113,133,120,140,220,130,149,124,172,129,139,136,103,110,108,128,118,128,116,121,122,117,125,135,162,163,133,118,159,127,123,165,158,115,167,164,129,138,101,117,101,84,178,156,114,134,122,144,94,111,130,173,158,162,122,97,150,171,123,125,143,139,194,181,122,101,115,153,109,162,132,161,114,98,116,159,133,144,111,115,99,98,115,144,116,171,157,153,182,100,141,153,116,141,145,121,130,173,166,141,117,147,136,80,187,116,124,137,153,128,95,133,113,161,90,148,115,76,84,138,161,111,147,149,178,188,140,118,130,107,119,91,184,121,178,139,136,132,139,77,143,91,187,132,134,131,137,103,121,104,143,103,153,193,178,124,165,107,107,130,125,120,101,201,140,131,199,186,151,128,135,132,128,142,189,145,103,126,145,123,129,145,142,111,150,133,145,127,170,175,119,100,136,127,92,174,101,103,143,126,154,122,163,140,158,164,133,139,113,115,141,133,150,141,123,113,97,164,130,111,85,109,138,93,156,75,110,92,117,144,147,134,137,152,151,109,144,155,114,94,93,149,150,168,116,98,135,146,103,134,145,112,112,166,158,128,132,151,190,134,205,119,132,130,165,149,147,124,145
TCV,alpha_mode_1,171.874,25.5520277865,211,207,162,203,217,152,202,176,155,163,188,203,164,190,160,168,200,180,210,185,161,116,174,159,147,231,149,179,190,143,214,194,157,184,167,147,178,168,145,110,177,255,176,202,82,138,204,163,137,136,155,147,158,174,191,137,152,177,181,209,170,185,163,196,205,209,163,189,182,207,214,194,192,171,159,200,145,113,214,170,139,171,169,149,186,184,206,146,134,154,201,172,142,190,191,120,136,155,178,162,149,188,186,149,176,154,144,171,161,125,169,173,200,142,179,161,183,193,178,163,142,175,131,229,164,203,135,149,182,174,148,142,194,218,207,147,196,187,172,148,98,164,141,180,195,185,122,136,195,175,125,150,156,165,163,148,153,156,245,195,193,165,177,201,200,192,154,151,199,160,190,173,161,176,186,173,182,135,184,169,123,144,203,138,173,140,175,187,144,204,161,176,157,150,203,176,138,198,175,210,135,154,120,142,143,153,193,132,129,122,159,171,159,155,193,153,181,206,131,154,193,194,177,196,157,161,191,159,160,221,137,187,167,206,189,147,203,154,128,173,125,157,153,173,153,203,163,184,169,193,155,206,171,163,192,151,169,189,183,151,205,174,158,198,165,158,162,132,184,182,224,187,145,149,178,169,159,203,188,176,221,201,150,208,197,168,208,145,233,179,172,184,159,194,152,177,160,169,210,161,201,174,196,216,167,175,170,120,153,169,200,227,177,190,167,200,141,181,136,196,199,201,145,162,152,194,137,216,167,167,167,145,202,161,158,178,197,179,249,162,161,187,189,190,149,204,157,196,173,160,170,185,154,172,167,161,186,150,179,198,177,157,170,228,142,211,137,179,179,160,129,118,173,168,159,177,182,152,109,187,159,200,161,145,141,203,177,204,188,229,177,164,121,194,158,158,207,176,136,186,145,142,187,131,172,165,158,185,174,166,200,159,159,174,199,218,130,179,166,121,139,172,145,194,171,195,186,159,194,185,167,130,137,207,188,148,152,227,161,139,172,155,211,174,144,203,170,148,188,159,157,142,151,144,148,190,226,125,192,191,173,194,172,177,155,213,195,195,175,150,148,183,208,197,145,202,144,190,131,176,185,198,193,182,171,151,225,158,165,179,187,191,180,155,167,170,171,160,175,176,188,186,136,145,182,160,160,151,150,138,193,163,136,139,170,192,160,147,144,158,138,166,154,181,197,141,153,182,149,197,199,168,159,195,147,149,130,188,136,173,130,182,185,187,151,189,201,148,194,153,194,203,182,190,199,200,167,151,159,136,168,185,158,207,149,164,160,184,153,137,208,181,181,197,150,138,150,195,153,166,166,160,187,158,173,175,204,143,176,189,188,150,173,202,179,176,121,206,161,213,149,181,173,147,193,156,172,166,179,183,156,153,157,135,201,159,174,166,212,158,164,183,142,146,156,193,155,179,137,144,178,180,177,148,184,177,191,183,147,170,223,177,210,200,142,130,215,150,153,107,142,175,179,186,149,161,161,205,166,167,194,175,156,178,160,204,145,182,153,198,172,177,182,192,164,149,165,194,149,185,159,205,181,205,207,132,187,228,182,167,134,193,185,205,217,206,158,154,141,171,171,221,187,172,170,169,154,183,171,155,165,147,169,190,149,230,128,173,205,166,189,204,229,191,156,161,152,172,177,214,176,148,169,165,184,233,167,203,163,210,170,186,181,146,144,146,165,160,168,151,162,153,153,166,174,195,215,168,177,213,172,161,190,202,152,216,216,145,167,140,146,136,122,213,186,159,167,154,194,135,150,173,216,204,208,153,136,171,220,162,172,192,181,245,214,164,146,149,211,150,183,175,195,158,141,155,198,180,180,148,159,126,141,169,195,156,200,194,188,196,140,178,209,154,159,189,158,172,195,209,179,152,179,198,117,216,163,154,182,186,159,129,178,141,183,122,205,152,111,111,175,186,140,182,177,199,226,181,155,170,152,140,130,223,157,234,184,179,165,167,107,195,125,223,167,181,171,171,133,162,143,181,141,189,227,211,163,204,153,142,173,157,161,128,247,163,177,219,224,190,172,178,165,161,191,229,189,146,166,189,169,172,173,189,155,171,174,179,163,206,220,165,147,176,175,129,217,139,140,190,178,183,163,200,188,179,207,170,163,150,155,179,165,205,176,162,154,128,201,162,157,126,148,182,129,197,112,154,134,155,185,188,185,192,174,187,142,183,185,158,130,126,181,181,207,152,128,170,182,139,182,180,154,148,199,200,171,167,175,229,184,248,147,161,168,210,192,192,164,179
LONELINESS,alpha_mode_1,0.73783016741,0.0166186874916,0.734220685475,0.750370747599,0.735409477491,0.731722994355,0.726002262592,0.724138030881,0.765162486032,0.744081635372,0.740991324706,0.729144494453,0.753470602468,0.72938471928,0.72109068527,0.754999013887,0.733181016744,0.732447718247,0.733941829769,0.74436664425,0.7679770799,0.746669129903,0.744517363746,0.735283024498,0.683882980908,0.734123082199,0.743136624509,0.757138906514,0.728679686725,0.72337802105,0.72176726302,0.709084475325,0.732075411265,0.761151516833,0.728051274784,0.718024495439,0.764678117617,0.744281734681,0.749762262142,0.745743503353,0.732973550259,0.755714153946,0.752704409674,0.73039451741,0.740626797378,0.720991486199,0.73642419893,0.747258718195,0.732652377225,0.72449462772,0.711170161796,0.751505256479,0.741452502239,0.743396746322,0.746737367354,0.722014751723,0.747222885948,0.72295281508,0.745006019828,0.763425091461,0.726761508617,0.72655737083,0.721404343857,0.741639674522,0.747823622054,0.741445723127,0.765550871744,0.742221211084,0.754954439954,0.725453708717,0.743440179691,0.760485520747,0.752289357457,0.754835493633,0.73673662555,0.72458768158,0.753856760416,0.7553061673,0.726021605224,0.750913311233,0.748452896975,0.743940736968,0.726892795257,0.740621392,0.72622095543,0.738261198452,0.735291836184,0.74347504702,0.751532631825,0.723488131068,0.720868314514,0.721538686422,0.72359264428,0.754400821105,0.75138439096,0.743442841166,0.734827417942,0.726123244654,0.73949789059,0.719911015011,0.734773369371,0.689830294614,0.727720038299,0.733983440187,0.746332615947,0.764389256567,0.712563183137,0.740242758155,0.732785536888,0.721511123685,0.754077904205,0.714977558851,0.759793979251,0.775505771283,0.753760956499,0.742314067243,0.725709097486,0.736304408737,0.726443298668,0.778559411251,0.750289755573,0.76049373528,0.73514528278,0.725319948127,0.749554835973,0.73021918435,0.732028591045,0.741583851596,0.71150755859,0.75074251173,0.731861059474,0.716183520791,0.705736436197,0.729441747299,0.718456327342,0.749783469112,0.740412487005,0.736454958235,0.744659206689,0.751079328018,0.745451229646,0.755328585733,0.75762278375,0.750277497542,0.736056216762,0.736299770742,0.726631133779,0.764867479929,0.751094065049,0.759674518757,0.769626586108,0.757751594964,0.765671556928,0.743042508563,0.736485010035,0.74921964317,0.723402236861,0.725887532902,0.739856750387,0.732499737352,0.741571482477,0.746863699437,0.732208131935,0.750700018979,0.739402863303,0.745108691543,0.741989171906,0.733677623107,0.705736562528,0.724302365068,0.721553855716,0.72332295072,0.73899954661,0.743626456475,0.738321402205,0.730342825108,0.741871515149,0.752836827442,0.74849611634,0.755506143581,0.731776505248,0.721835275288,0.711125396779,0.784991454197,0.721510523063,0.683513547937,0.753407493396,0.743402133364,0.724970534894,0.751869366129,0.738241968284,0.747739422038,0.741339570816,0.729794498962,0.752164239466,0.764863498207,0.738898644801,0.751590980675,0.735829840196,0.725517400903,0.743042205354,0.744403624473,0.732810819508,0.708317079333,0.72985477129,0.726608940232,0.730915679981,0.750426525507,0.722644657809,0.722434373831,0.738051347881,0.750116590129,0.746415270408,0.746548780594,0.733939950596,0.73823137977,0.734620508838,0.70717560787,0.706455746201,0.748643858784,0.708586898934,0.724329753603,0.7287735743,0.756017636666,0.74919653739,0.731378824346,0.720387487281,0.746649834137,0.72971321839,0.708119768442,0.751124779818,0.771482093609,0.732095737816,0.74988206494,0.730563824956,0.748917770704,0.76210818193,0.74519182597,0.738865131144,0.701016840988,0.763832343211,0.740208333907,0.732689914848,0.701891961957,0.712049479006,0.737357896492,0.738604299788,0.742839539055,0.742765778557,0.762875420725,0.76878561861,0.749121692398,0.738354365156,0.705690021027,0.734875266495,0.72999837523,0.744662383929,0.757458519668,0.733569622452,0.742877988859,0.743109466809,0.728967881719,0.7627314182,0.717718770406,0.731350174574,0.762590577183,0.738079227742,0.70491661461,0.743061608927,0.750840659432,0.737084077839,0.718736092576,0.742572512954,0.766361148061,0.753585952042,0.758582021972,0.726159854772,0.717561495209,0.749284282828,0.73496747829,0.730893741339,0.712652279864,0.735062360632,0.758254292224,0.711449732453,0.751497438912,0.726528275481,0.758444944325,0.771952841486,0.732847965245,0.733856594624,0.766222613845,0.739935856168,0.753220418667,0.740213632021,0.747150036617,0.714377196072,0.739279766348,0.729083186203,0.742298401864,0.742412802732,0.755622851544,0.763947093112,0.752243945763,0.74698296773,0.75876628909,0.752068409968,0.74284706912,0.74985181547,0.729086786045,0.746600239616,0.717380247017,0.756200467638,0.764381751378,0.738964728813,0.725280605186,0.712739320135,0.767764898701,0.716288508871,0.757674728664,0.75284711262,0.760503970695,0.719573181247,0.718041993035,0.73498523677,0.761456282272,0.7266481423,0.720164834601,0.725315123852,0.739721762486,0.718859482921,0.779871662688,0.742980451363,0.74315319205,0.751421678889,0.74299798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