import os
from pathlib import Path

from sklearn.base import BaseEstimator

import quapy as qp
import numpy as np

from method.aggregative import KDEyML
from quapy.functional import l1_norm, ILRtransformation
from scipy.stats import entropy



def fetch_UCI_multiclass(data_name):
    return qp.datasets.fetch_UCIMulticlassDataset(data_name, min_class_support=0.01)


def fetch_UCI_binary(data_name):
    return qp.datasets.fetch_UCIBinaryDataset(data_name)

# global configurations

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

multiclass = {
    'datasets': qp.datasets.UCI_MULTICLASS_DATASETS,
    'fetch_fn': fetch_UCI_multiclass,
    'sample_size': 1000
}
multiclass['datasets'].remove('poker_hand')  # random performance
multiclass['datasets'].remove('hcv')  # random performance
multiclass['datasets'].remove('letter')  # many classes
multiclass['datasets'].remove('isolet')  # many classes


# utils
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'


def normalized_entropy(p):
    """
    Normalized Shannon entropy in [0, 1]
    p: array-like, prevalence vector (sums to 1)
    """
    p = np.asarray(p)
    H = entropy(p) # Shannon entropy
    H_max = np.log(len(p))
    return np.clip(H / H_max, 0, 1)


def antagonistic_prevalence(p, strength=1):
    ilr = ILRtransformation()
    z = ilr(p)
    z_ant = - strength * z
    p_ant = ilr.inverse(z_ant)
    return p_ant


class KDEyCLR(KDEyML):
    def __init__(self, classifier: BaseEstimator=None, fit_classifier=True, val_split=5, bandwidth=1., random_state=None):
        super().__init__(
            classifier=classifier, fit_classifier=fit_classifier, val_split=val_split, bandwidth=bandwidth,
            random_state=random_state, kernel='aitchison'
        )


class KDEyILR(KDEyML):
    def __init__(self, classifier: BaseEstimator=None, fit_classifier=True, val_split=5, bandwidth=1., random_state=None):
        super().__init__(
            classifier=classifier, fit_classifier=fit_classifier, val_split=val_split, bandwidth=bandwidth,
            random_state=random_state, kernel='ilr'
        )