import argparse
import random
import statistics

import utils as utils
from joblib import Parallel, delayed
from sklearn import svm
from sklearn.preprocessing import StandardScaler
from sklearn.utils import shuffle

parser = argparse.ArgumentParser()
parser.add_argument("-dataset", default="../data/features.csv")
parser.add_argument("-sample_size", default=40)
parser.add_argument(
    "-random_state", default=42, type=int
)  # random state for reproducability
parser.add_argument("-jobs", default=6, type=int)  # parallelization parameter
parser.add_argument(
    "-classifier", default="svm"
)  # classifier svm, random_forest, neural_network, knn
args = parser.parse_args()

EERS = []

for random_state in range(100):
    (
        users,
        user_touches,
        user_touches_shuffled,
        session_user_touches,
    ) = utils.preprocessing(
        dataset_path=args.dataset,
        game="swipe",
        direction="left",
        random_state=random_state,
        phone="iPhone 7",
    )

    random.Random(random_state).shuffle(users)
    subsampled_users = users[: args.sample_size]

    def user_exclude_eer(user):
        if len(session_user_touches[user]) < 2:
            return

        # Split into 2 equal user groups
        users_copy = list(users.copy())
        users_copy.remove(user)
        user_groups = utils.partition_list(users_copy)

        X_train, X_test, y_train, y_test = utils.dedicated_sessions(
            session_user_touches,
            user_touches_shuffled,
            user,
            train_users=user_groups[0],
            test_users=user_groups[1],
        )

        scaler = StandardScaler()
        X_train = scaler.fit_transform(X_train)
        X_test = scaler.transform(X_test)

        y_pred = utils.classify(X_train, y_train, X_test, classifier=args.classifier)

        eer = utils.calculate_eer(y_test, y_pred)
        return eer

    EERS.append(
        statistics.mean(
            [
                x
                for x in Parallel(n_jobs=args.jobs)(
                    [delayed(user_exclude_eer)(user) for user in subsampled_users]
                )
                if x is not None
            ]
        )
    )

utils.export_csv("../results/" + args.classifier + "/general/realistic.csv", EERS)
