"""Parsing the hyperparameters."""

import argparse

def parameter_parser():
    """
    A method to parse up command line parameters.
    By default it gives an embedding of the Facebook politicians network.
    """
    parser = argparse.ArgumentParser(description="Run GEMSEC.")

    parser.add_argument("--input",
                        nargs="?",
                        default="./data/politician_edges.csv",
	                help="Input graph path.")

    parser.add_argument("--embedding-output",
                        nargs="?",
                        default="./output/embeddings/politician_embedding.csv",
	                help="Embeddings path.")

    parser.add_argument("--cluster-mean-output",
                        nargs="?",
                        default="./output/cluster_means/politician_means.csv",
	                help="Cluster means path.")

    parser.add_argument("--log-output",
                        nargs="?",
                        default="./output/logs/politician.json",
	                help="Log path.")

    parser.add_argument("--assignment-output",
                        nargs="?",
                        default="./output/assignments/politician.json",
	                help="Log path.")

    parser.add_argument("--dump-matrices",
                        type=bool,
                        default=True,
	                help="Save the embeddings to disk or not. Default is not.")

    parser.add_argument("--model",
                        nargs="?",
                        default="GEMSECWithRegularization",
	                help="The model type.")

    parser.add_argument("--P",
                        type=float,
                        default=1,
	                help="Return hyperparameter. Default is 1.")

    parser.add_argument("--Q",
                        type=float,
                        default=1,
	                help="In-out hyperparameter. Default is 1.")

    parser.add_argument("--walker",
                        nargs="?",
                        default="first",
	                help="Random walker order. Default is first.")

    parser.add_argument("--dimensions",
                        type=int,
                        default=16,
	                help="Number of dimensions. Default is 16.")

    parser.add_argument("--random-walk-length",
                        type=int,
                        default=80,
	                help="Length of random walk per source. Default is 80.")

    parser.add_argument("--num-of-walks",
                        type=int,
                        default=5,
	                help="Number of random walks per source. Default is 5.")

    parser.add_argument("--window-size",
                        type=int,
                        default=5,
                    	help="Window size for proximity statistic extraction. Default is 5.")

    parser.add_argument("--distortion",
                        type=float,
                        default=0.75,
	                help="Downsampling distortion. Default is 0.75.")

    parser.add_argument("--negative-sample-number",
                        type=int,
                        default=10,
	                help="Number of negative samples to draw. Default is 10.")

    parser.add_argument("--initial-learning-rate",
                        type=float,
                        default=0.01,
	                help="Initial learning rate. Default is 0.01.")

    parser.add_argument("--minimal-learning-rate",
                        type=float,
                        default=0.001,
	                help="Minimal learning rate. Default is 0.001.")

    parser.add_argument("--annealing-factor",
                        type=float,
                        default=1,
	                help="Annealing factor. Default is 1.0.")

    parser.add_argument("--initial-gamma",
                        type=float,
                        default=0.1,
	                help="Initial clustering weight. Default is 0.1.")

    parser.add_argument("--final-gamma",
                        type=float,
                        default=0.5,
	                help="Final clustering weight. Default is 0.5.")

    parser.add_argument("--lambd",
                        type=float,
                        default=2.0**-4,
	                help="Smoothness regularization penalty. Default is 0.0625.")

    parser.add_argument("--cluster-number",
                        type=int,
                        default=20,
	                help="Number of clusters. Default is 20.")

    parser.add_argument("--overlap-weighting",
                        nargs="?",
                        default="normalized_overlap",
	                help="Weight construction technique for regularization.")

    parser.add_argument("--regularization-noise",
                        type=float,
                        default=10**-8,
	                help="Uniform noise max and min on the feature vector distance.")

    return parser.parse_args()

ab4d166faa9f54ce8c0a085b6c5c0986ba80b563
<mask token>

<mask token>


def parameter_parser():
    """
    A method to parse up command line parameters.
    By default it gives an embedding of the Facebook politicians network.
    """
    parser = argparse.ArgumentParser(description='Run GEMSEC.')
    parser.add_argument('--input', nargs='?', default=
        './data/politician_edges.csv', help='Input graph path.')
    parser.add_argument('--embedding-output', nargs='?', default=
        './output/embeddings/politician_embedding.csv', help='Embeddings path.'
        )
    parser.add_argument('--cluster-mean-output', nargs='?', default=
        './output/cluster_means/politician_means.csv', help=
        'Cluster means path.')
    parser.add_argument('--log-output', nargs='?', default=
        './output/logs/politician.json', help='Log path.')
    parser.add_argument('--assignment-output', nargs='?', default=
        './output/assignments/politician.json', help='Log path.')
    parser.add_argument('--dump-matrices', type=bool, default=True, help=
        'Save the embeddings to disk or not. Default is not.')
    parser.add_argument('--model', nargs='?', default=
        'GEMSECWithRegularization', help='The model type.')
    parser.add_argument('--P', type=float, default=1, help=
        'Return hyperparameter. Default is 1.')
    parser.add_argument('--Q', type=float, default=1, help=
        'In-out hyperparameter. Default is 1.')
    parser.add_argument('--walker', nargs='?', default='first', help=
        'Random walker order. Default is first.')
    parser.add_argument('--dimensions', type=int, default=16, help=
        'Number of dimensions. Default is 16.')
    parser.add_argument('--random-walk-length', type=int, default=80, help=
        'Length of random walk per source. Default is 80.')
    parser.add_argument('--num-of-walks', type=int, default=5, help=
        'Number of random walks per source. Default is 5.')
    parser.add_argument('--window-size', type=int, default=5, help=
        'Window size for proximity statistic extraction. Default is 5.')
    parser.add_argument('--distortion', type=float, default=0.75, help=
        'Downsampling distortion. Default is 0.75.')
    parser.add_argument('--negative-sample-number', type=int, default=10,
        help='Number of negative samples to draw. Default is 10.')
    parser.add_argument('--initial-learning-rate', type=float, default=0.01,
        help='Initial learning rate. Default is 0.01.')
    parser.add_argument('--minimal-learning-rate', type=float, default=
        0.001, help='Minimal learning rate. Default is 0.001.')
    parser.add_argument('--annealing-factor', type=float, default=1, help=
        'Annealing factor. Default is 1.0.')
    parser.add_argument('--initial-gamma', type=float, default=0.1, help=
        'Initial clustering weight. Default is 0.1.')
    parser.add_argument('--final-gamma', type=float, default=0.5, help=
        'Final clustering weight. Default is 0.5.')
    parser.add_argument('--lambd', type=float, default=2.0 ** -4, help=
        'Smoothness regularization penalty. Default is 0.0625.')
    parser.add_argument('--cluster-number', type=int, default=20, help=
        'Number of clusters. Default is 20.')
    parser.add_argument('--overlap-weighting', nargs='?', default=
        'normalized_overlap', help=
        'Weight construction technique for regularization.')
    parser.add_argument('--regularization-noise', type=float, default=10 **
        -8, help='Uniform noise max and min on the feature vector distance.')
    return parser.parse_args()

<mask token>
import argparse


def parameter_parser():
    """
    A method to parse up command line parameters.
    By default it gives an embedding of the Facebook politicians network.
    """
    parser = argparse.ArgumentParser(description='Run GEMSEC.')
    parser.add_argument('--input', nargs='?', default=
        './data/politician_edges.csv', help='Input graph path.')
    parser.add_argument('--embedding-output', nargs='?', default=
        './output/embeddings/politician_embedding.csv', help='Embeddings path.'
        )
    parser.add_argument('--cluster-mean-output', nargs='?', default=
        './output/cluster_means/politician_means.csv', help=
        'Cluster means path.')
    parser.add_argument('--log-output', nargs='?', default=
        './output/logs/politician.json', help='Log path.')
    parser.add_argument('--assignment-output', nargs='?', default=
        './output/assignments/politician.json', help='Log path.')
    parser.add_argument('--dump-matrices', type=bool, default=True, help=
        'Save the embeddings to disk or not. Default is not.')
    parser.add_argument('--model', nargs='?', default=
        'GEMSECWithRegularization', help='The model type.')
    parser.add_argument('--P', type=float, default=1, help=
        'Return hyperparameter. Default is 1.')
    parser.add_argument('--Q', type=float, default=1, help=
        'In-out hyperparameter. Default is 1.')
    parser.add_argument('--walker', nargs='?', default='first', help=
        'Random walker order. Default is first.')
    parser.add_argument('--dimensions', type=int, default=16, help=
        'Number of dimensions. Default is 16.')
    parser.add_argument('--random-walk-length', type=int, default=80, help=
        'Length of random walk per source. Default is 80.')
    parser.add_argument('--num-of-walks', type=int, default=5, help=
        'Number of random walks per source. Default is 5.')
    parser.add_argument('--window-size', type=int, default=5, help=
        'Window size for proximity statistic extraction. Default is 5.')
    parser.add_argument('--distortion', type=float, default=0.75, help=
        'Downsampling distortion. Default is 0.75.')
    parser.add_argument('--negative-sample-number', type=int, default=10,
        help='Number of negative samples to draw. Default is 10.')
    parser.add_argument('--initial-learning-rate', type=float, default=0.01,
        help='Initial learning rate. Default is 0.01.')
    parser.add_argument('--minimal-learning-rate', type=float, default=
        0.001, help='Minimal learning rate. Default is 0.001.')
    parser.add_argument('--annealing-factor', type=float, default=1, help=
        'Annealing factor. Default is 1.0.')
    parser.add_argument('--initial-gamma', type=float, default=0.1, help=
        'Initial clustering weight. Default is 0.1.')
    parser.add_argument('--final-gamma', type=float, default=0.5, help=
        'Final clustering weight. Default is 0.5.')
    parser.add_argument('--lambd', type=float, default=2.0 ** -4, help=
        'Smoothness regularization penalty. Default is 0.0625.')
    parser.add_argument('--cluster-number', type=int, default=20, help=
        'Number of clusters. Default is 20.')
    parser.add_argument('--overlap-weighting', nargs='?', default=
        'normalized_overlap', help=
        'Weight construction technique for regularization.')
    parser.add_argument('--regularization-noise', type=float, default=10 **
        -8, help='Uniform noise max and min on the feature vector distance.')
    return parser.parse_args()

"""Parsing the hyperparameters."""

import argparse

def parameter_parser():
    """
    A method to parse up command line parameters.
    By default it gives an embedding of the Facebook politicians network.
    """
    parser = argparse.ArgumentParser(description="Run GEMSEC.")

    parser.add_argument("--input",
                        nargs="?",
                        default="./data/politician_edges.csv",
	                help="Input graph path.")

    parser.add_argument("--embedding-output",
                        nargs="?",
                        default="./output/embeddings/politician_embedding.csv",
	                help="Embeddings path.")

    parser.add_argument("--cluster-mean-output",
                        nargs="?",
                        default="./output/cluster_means/politician_means.csv",
	                help="Cluster means path.")

    parser.add_argument("--log-output",
                        nargs="?",
                        default="./output/logs/politician.json",
	                help="Log path.")

    parser.add_argument("--assignment-output",
                        nargs="?",
                        default="./output/assignments/politician.json",
	                help="Log path.")

    parser.add_argument("--dump-matrices",
                        type=bool,
                        default=True,
	                help="Save the embeddings to disk or not. Default is not.")

    parser.add_argument("--model",
                        nargs="?",
                        default="GEMSECWithRegularization",
	                help="The model type.")

    parser.add_argument("--P",
                        type=float,
                        default=1,
	                help="Return hyperparameter. Default is 1.")

    parser.add_argument("--Q",
                        type=float,
                        default=1,
	                help="In-out hyperparameter. Default is 1.")

    parser.add_argument("--walker",
                        nargs="?",
                        default="first",
	                help="Random walker order. Default is first.")

    parser.add_argument("--dimensions",
                        type=int,
                        default=16,
	                help="Number of dimensions. Default is 16.")

    parser.add_argument("--random-walk-length",
                        type=int,
                        default=80,
	                help="Length of random walk per source. Default is 80.")

    parser.add_argument("--num-of-walks",
                        type=int,
                        default=5,
	                help="Number of random walks per source. Default is 5.")

    parser.add_argument("--window-size",
                        type=int,
                        default=5,
                    	help="Window size for proximity statistic extraction. Default is 5.")

    parser.add_argument("--distortion",
                        type=float,
                        default=0.75,
	                help="Downsampling distortion. Default is 0.75.")

    parser.add_argument("--negative-sample-number",
                        type=int,
                        default=10,
	                help="Number of negative samples to draw. Default is 10.")

    parser.add_argument("--initial-learning-rate",
                        type=float,
                        default=0.01,
	                help="Initial learning rate. Default is 0.01.")

    parser.add_argument("--minimal-learning-rate",
                        type=float,
                        default=0.001,
	                help="Minimal learning rate. Default is 0.001.")

    parser.add_argument("--annealing-factor",
                        type=float,
                        default=1,
	                help="Annealing factor. Default is 1.0.")

    parser.add_argument("--initial-gamma",
                        type=float,
                        default=0.1,
	                help="Initial clustering weight. Default is 0.1.")

    parser.add_argument("--final-gamma",
                        type=float,
                        default=0.5,
	                help="Final clustering weight. Default is 0.5.")

    parser.add_argument("--lambd",
                        type=float,
                        default=2.0**-4,
	                help="Smoothness regularization penalty. Default is 0.0625.")

    parser.add_argument("--cluster-number",
                        type=int,
                        default=20,
	                help="Number of clusters. Default is 20.")

    parser.add_argument("--overlap-weighting",
                        nargs="?",
                        default="normalized_overlap",
	                help="Weight construction technique for regularization.")

    parser.add_argument("--regularization-noise",
                        type=float,
                        default=10**-8,
	                help="Uniform noise max and min on the feature vector distance.")

    return parser.parse_args()
