import argparse
import pickle
from sklearn.linear_model import LogisticRegression as LR
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants


# LeQua official baselines for task T1B (Multiclass/Vector)
# =========================================================

def baselines():
    yield CC(LR(n_jobs=-1)), "CC"
    yield ACC(LR(n_jobs=-1)), "ACC"
    yield PCC(LR(n_jobs=-1)), "PCC"
    yield PACC(LR(n_jobs=-1)), "PACC"
    yield EMQ(CalibratedClassifierCV(LR(), n_jobs=-1)), "SLD"
    yield HDy(LR(n_jobs=-1)) if args.task == 'T1A' else OneVsAll(HDy(LR()), n_jobs=-1), "HDy"


def main(args):

    models_path = qp.util.create_if_not_exist(os.path.join(args.modeldir, args.task))

    path_dev_vectors = os.path.join(args.datadir, 'dev_vectors')
    path_dev_prevs = os.path.join(args.datadir, 'dev_prevalences.csv')
    path_train = os.path.join(args.datadir, 'training_vectors.txt')

    qp.environ['SAMPLE_SIZE'] = constants.SAMPLE_SIZE[args.task]

    train = LabelledCollection.load(path_train, load_binary_vectors)
    nF = train.instances.shape[1]

    print(f'number of classes: {len(train.classes_)}')
    print(f'number of training documents: {len(train)}')
    print(f'training prevalence: {F.strprev(train.prevalence())}')
    print(f'training matrix shape: {train.instances.shape}')

    param_grid = {
        'C': np.logspace(-3,3,7),
        'class_weight': ['balanced', None]
    }

    def gen_samples():
        return gen_load_samples_T1(path_dev_vectors, nF, ground_truth_path=path_dev_prevs, return_id=False)

    for quantifier, q_name in baselines():
        print(f'{q_name}: Model selection')
        quantifier = qp.model_selection.GridSearchQ(
            quantifier,
            param_grid,
            sample_size=None,
            protocol='gen',
            error=qp.error.mae,
            refit=False,
            verbose=True
        ).fit(train, gen_samples)

        print(f'{q_name} got MAE={quantifier.best_score_:.3f} (hyper-params: {quantifier.best_params_})')

        model_path = os.path.join(models_path, q_name+'.pkl')
        print(f'saving model in {model_path}')
        pickle.dump(quantifier.best_model(), open(model_path, 'wb'), protocol=pickle.HIGHEST_PROTOCOL)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='LeQua2022 Task T1A/T1B baselines')
    parser.add_argument('task', metavar='TASK', type=str, choices=['T1A', 'T1B'],
                        help='Task name (T1A, T1B)')
    parser.add_argument('datadir', metavar='DATA-PATH', type=str,
                        help='Path of the directory containing "dev_prevalences.csv", "training_vectors.txt", and '
                             'the directory "dev_vectors"')
    parser.add_argument('modeldir', metavar='MODEL-PATH', type=str,
                        help='Path where to save the models. '
                             'A subdirectory named <task> will be automatically created.')
    args = parser.parse_args()

    if not os.path.exists(args.datadir):
        raise FileNotFoundError(f'path {args.datadir} does not exist')
    if not os.path.isdir(args.datadir):
        raise ValueError(f'path {args.datadir} is not a valid directory')
    if not os.path.exists(os.path.join(args.datadir, "dev_prevalences.csv")):
        raise FileNotFoundError(f'path {args.datadir} does not contain "dev_prevalences.csv" file')
    if not os.path.exists(os.path.join(args.datadir, "training_vectors.txt")):
        raise FileNotFoundError(f'path {args.datadir} does not contain "training_vectors.txt" file')
    if not os.path.exists(os.path.join(args.datadir, "dev_vectors")):
        raise FileNotFoundError(f'path {args.datadir} does not contain "dev_vectors" folder')

    main(args)