import argparse

import utils as utils
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(
    "-random_state", default=42, type=int
)  # random state for reproducability
parser.add_argument(
    "-classifier", default="svm"
)  # classifier svm, random_forest, neural_network, knn
args = parser.parse_args()

users, user_touches, user_touches_shuffled, session_user_touches = utils.preprocessing(
    dataset_path=args.dataset,
    game="swipe",
    direction="left",
    random_state=args.random_state,
)

EERS = []
sanitize_length_column = []

for sanitize_length in range(2, 32):
    lusers = 0
    for user in users:
        if len(user_touches[user]) < 10:
            continue

        is_long = len(session_user_touches[user]) == 31

        if not is_long:
            continue

        lusers += 1

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

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

        X_train, y_train = shuffle(X_train, y_train, random_state=args.random_state)
        X_test, y_test = shuffle(X_test, y_test, random_state=args.random_state)

        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)

        EERS.append(eer)

    sanitize_length_column.extend([sanitize_length] * lusers)

utils.export_csv_two_columns(
    "../results/" + args.classifier + "/p1_sessions/sessions_performed.csv",
    "sanitize_length",
    "eer",
    sanitize_length_column,
    EERS,
)
