search-telecom-churn-logreg.py

#!/usr/bin/env python

from multiprocessing import Lock

from scipy.stats import randint as randint

from utils import loguniform, parse_args, read_telecom_churn,\
    evaluate_logreg_experiment, run_pool, generate_random_experiments

if __name__ == "__main__":
    parameter_space = {
        'clf__C': loguniform(-5, 3, 10),
        'clf__class_weight': loguniform(-2, 2, 10),
        'clf__dual': [False],
        'clf__fit_intercept': [True, False],
        'clf__intercept_scaling': [1],
        'clf__max_iter': [500],
        'clf__multi_class': ['warn'],
        'clf__n_jobs': [1],
        'clf__penalty': ['l2'],
        'clf__random_state': randint(1, 100000),
        'clf__solver': ['lbfgs'],
        'clf__tol': loguniform(-10, -2, 10),
        'clf__verbose': [0],
        'clf__warm_start': [False]
    }

    args = parse_args()
    log_lock = Lock()
    X_train, X_val, y_train, y_val, folds = read_telecom_churn()

    # captures data and lock to use in forked processes
    def evaluator(experiment):
        evaluate_logreg_experiment(
            experiment,
            experiment_name=args.name,
            log_file=args.log,
            log_lock=log_lock,
            X_train=X_train, X_val=X_val, y_train=y_train, y_val=y_val,
            folds=folds
        )

    run_pool(
        generate_random_experiments(parameter_space, args.iterations),
        args,
        evaluator)