import multiprocessing

if __name__ == '__main__':

    multiprocessing.set_start_method('forkserver')

    import sys

    from sklearn.ensemble import RandomForestClassifier

    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])

    # Read the data set into meory

    # parameter variation

    hyper_params = [{

       'n_estimators': [500],

       'min_samples_leaf': np.logspace(-4,-1,4),

       'max_features': ('sqrt','log2',None),

    }]

    # hyper_params = {

    #    'n_estimators': [100,500],

    #    'criterion': ('gini',)

    # }

    # create the classifier

    clf = RandomForestClassifier(class_weight='balanced',n_jobs=1)

    # evaluate the model

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