import multiprocessing

if __name__ == '__main__':

    multiprocessing.set_start_method('forkserver')

    import sys

    from sklearn.feature_selection import f_classif

    # from sklearn.linear_model import LogisticRegression

    from p_values_for_logreg import LogisticReg

    from sklearn.preprocessing import StandardScaler

    import pdb

    import numpy as np

    from read_file import read_file

    dataset = sys.argv[1]

    save_file = sys.argv[2]

    random_state = int(sys.argv[3])

    rare = eval(sys.argv[4])

    longitudinal=False

    features, labels, pt_ids, feature_names, zfile = read_file(dataset,longitudinal,rare)

    scores=[]

    # scale the data

    features = StandardScaler().fit_transform(features)

    # create the classifier

    for i in np.arange(features.shape[1]):

        print('fitting feature',feature_names[i],i,'of',features.shape[1])

        est = LogisticReg(solver='saga',

                                 C = 1000, 

                                 random_state=random_state)

        est.fit(features[:,i].reshape(-1,1),labels)

        print('pvalue:',est.p_values[0],

              'weight:',np.abs(est.model.coef_.flatten()[0]))

        if est.p_values[0] < 0.05:

            scores.append(np.abs(est.model.coef_.flatten()[0]))

        else:

            scores.append(0)

    # save file

    out_text=''

    param_string = ','.join(['{}={}'.format(p, v) 

                             for p,v in est.model.get_params().items()])

    # algorithm seed    feature score

    for i,c in enumerate(scores):

        out_text += '\t'.join(['Univariate LR',

                               param_string,

                               str(random_state),

                               feature_names[i],

                               str(c)])+'\n'

    import os

    if os.path.exists(save_file.split('.csv')[0]+'.imp_score'):

        os.remove(save_file.split('.csv')[0]+'.imp_score')

    if os.path.exists(save_file.split('.csv')[0]+'.roc'):

        os.remove(save_file.split('.csv')[0]+'.roc')

    ext = '.univariate_score'

    with open(save_file.split('.csv')[0] + ext,'w') as out:

        out.write('algorithm\talg-parameters\tseed\tfeature\tscore\n')

        out.write(out_text)