import os
import warnings
from os.path import join
from pathlib import Path

from sklearn.calibration import CalibratedClassifierCV
from sklearn.linear_model import LogisticRegression as LR
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from copy import deepcopy as cp
import quapy as qp
from BayesianKDEy._bayeisan_kdey import BayesianKDEy
from quapy.method.aggregative import DistributionMatchingY as DMy
from quapy.method.base import BinaryQuantifier
from quapy.model_selection import GridSearchQ
from quapy.data import Dataset
# from BayesianKDEy.plot_simplex import plot_prev_points, plot_prev_points_matplot
from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ, WithConfidenceABC, AggregativeBootstrap
from quapy.functional import strprev
from quapy.method.aggregative import KDEyML, ACC
from quapy.protocol import UPP
import quapy.functional as F
import numpy as np
from tqdm import tqdm
from scipy.stats import dirichlet
from collections import defaultdict
from time import time
from sklearn.base import clone


# def new_classifier(training):
#     print('optimizing hyperparameters of Logistic Regression')
#     mod_sel = GridSearchCV(
#         estimator=LogisticRegression(),
#         param_grid={
#             'C': np.logspace(-4, 4, 9),
#             'class_weight': ['balanced', None]
#         },
#         cv=StratifiedKFold(n_splits=10, shuffle=True, random_state=0),
#         n_jobs=-1,
#         refit=False,
#     )
#     mod_sel.fit(*training.Xy)
#     # optim = LogisticRegression(**mod_sel.best_params_)
#     print(f'Done: hyperparameters chosen={mod_sel.best_params_}')
#     # calib = CalibratedClassifierCV(optim, cv=10, n_jobs=-1, ensemble=False).fit(*training.Xy)
#     # return calib
#     return LogisticRegression(**mod_sel.best_params_)

def methods():
    acc_hyper = {}
    hdy_hyper = {'n_bins': [3,4,5,8,16,32]}
    kdey_hyper = {'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
    wrap_hyper = lambda dic: {f'quantifier__{k}':v for k,v in dic.items()}
    # yield 'BootstrapACC', AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), wrap_hyper(acc_hyper)
    yield 'BootstrapHDy', AggregativeBootstrap(DMy(LR(), divergence='HD'), n_test_samples=1000, random_state=0), wrap_hyper(hdy_hyper)
    #yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(LR()), n_test_samples=1000, random_state=0), wrap_hyper(kdey_hyper)
    # yield 'BayesianACC', BayesianCC(LR(), mcmc_seed=0), acc_hyper
    # yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper
    # yield 'BayesianKDEy', BayesianKDEy(LR(), mcmc_seed=0), kdey_hyper


def experiment(dataset: Dataset, method: WithConfidenceABC, grid: dict):
    with qp.util.temp_seed(0):
        # model selection
        train, test = dataset.train_test
        train_prevalence = train.prevalence()
        if len(grid)>0:
            train, val  = train.split_stratified(train_prop=0.6, random_state=0)
            mod_sel = GridSearchQ(
                model=method,
                param_grid=grid,
                protocol=qp.protocol.UPP(val, repeats=250, random_state=0),
                refit=True,
                n_jobs=-1,
                verbose=True
            ).fit(*train.Xy)
            optim_quantifier = mod_sel.best_model()
            best_params = mod_sel.best_params_
            best_score = mod_sel.best_score_
            tr_time = mod_sel.refit_time_
        else:
            t_init = time()
            method.fit(*train.Xy)
            tr_time = time() - t_init
            best_params, best_score = {}, -1
            optim_quantifier = method

        # test
        results = defaultdict(list)
        test_generator = UPP(test, repeats=500, random_state=0)
        for i, (sample_X, true_prevalence) in tqdm(enumerate(test_generator()), total=test_generator.total(), desc=f'{method_name} predictions'):
            t_init = time()
            point_estimate, region = optim_quantifier.predict_conf(sample_X)
            ttime = time()-t_init
            results['true-prevs'].append(true_prevalence)
            results['point-estim'].append(point_estimate)
            results['shift'].append(qp.error.ae(true_prevalence, train_prevalence))
            results['ae'].append(qp.error.ae(prevs_true=true_prevalence, prevs_hat=point_estimate))
            results['rae'].append(qp.error.rae(prevs_true=true_prevalence, prevs_hat=point_estimate))
            results['coverage'].append(region.coverage(true_prevalence))
            results['amplitude'].append(region.montecarlo_proportion(n_trials=50_000))
            results['test-time'].append(ttime)
            results['samples'].append(region.samples)

        report = {
            'optim_hyper': best_params,
            'optim_score': best_score,
            'refit_time': tr_time,
            'train-prev': train_prevalence,
            'results': {k:np.asarray(v) for k,v in results.items()}
        }

        return report


def experiment_path(dir:Path, dataset_name:str, method_name:str):
    os.makedirs(dir, exist_ok=True)
    return dir/f'{dataset_name}__{method_name}.pkl'


if __name__ == '__main__':

    binary = {
        'datasets': qp.datasets.UCI_BINARY_DATASETS,
        'fetch_fn': qp.datasets.fetch_UCIBinaryDataset,
        'sample_size': 500
    }

    multiclass = {
        'datasets': qp.datasets.UCI_MULTICLASS_DATASETS,
        'fetch_fn': qp.datasets.fetch_UCIMulticlassDataset,
        'sample_size': 1000
    }

    result_dir = Path('./results')

    for setup in [binary, multiclass]:
        qp.environ['SAMPLE_SIZE'] = setup['sample_size']
        for data_name in setup['datasets']:
            data = setup['fetch_fn'](data_name)
            is_binary = data.n_classes==2
            result_subdir = result_dir / ('binary' if is_binary else 'multiclass')
            for method_name, method, hyper_params in methods():
                if isinstance(method, BinaryQuantifier) and not is_binary:
                    continue
                result_path = experiment_path(result_subdir, data_name, method_name)
                report = qp.util.pickled_resource(result_path, experiment, data, method, hyper_params)
                print(f'dataset={data_name}, '
                      f'method={method_name}: '
                      f'mae={report["results"]["ae"].mean():.3f}, '
                      f'coverage={report["results"]["coverage"].mean():.3f}, '
                      f'amplitude={report["results"]["amplitude"].mean():.3f}, ')