import multiprocessing

if __name__ == '__main__':

    multiprocessing.set_start_method('forkserver')

    import sys

    from sklearn.linear_model import LogisticRegression

    from sklearn.preprocessing import StandardScaler

    from sklearn.pipeline import Pipeline

    import pdb

    from evaluate_model import evaluate_model

    import numpy as np

    dataset = sys.argv[1]

    save_file = sys.argv[2]

    random_seed = int(sys.argv[3])

    rare = eval(sys.argv[4])

    # create the classifier

    clf = Pipeline([('scale',StandardScaler()),

                     ('lr', LogisticRegression(solver='saga',

                                               max_iter=1000,

                                               random_state=random_seed))

                     ])

    hyper_params = {

            'lr__C': np.logspace(-2,1,20),

            'lr__penalty': ['l1','l2'] 

            }

    # evaluate the model

    evaluate_model(dataset, save_file, random_seed, clf, 'ScaleLR', hyper_params, False,rare=rare)