import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.base import BinaryQuantifier
from quapy.model_selection import GridSearchQ
from quapy.method.aggregative import AggregativeProbabilisticQuantifier
from quapy.protocol import APP
import numpy as np
from sklearn.linear_model import LogisticRegression


# Define a custom quantifier: for this example, we will consider a new quantification algorithm that uses a
# logistic regressor for generating posterior probabilities, and then applies a custom threshold value to the
# posteriors. Since the quantifier internally uses a classifier, it is an aggregative quantifier; and since it
# relies on posterior probabilities, it is a probabilistic-aggregative quantifier. Note also it has an
# internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier
# is binary, for simplicity.

class MyQuantifier(AggregativeProbabilisticQuantifier, BinaryQuantifier):
    def __init__(self, classifier, alpha=0.5):
        self.alpha = alpha
        # aggregative quantifiers have an internal self.classifier attribute
        self.classifier = classifier

    def fit(self, data: LabelledCollection, fit_classifier=True):
        assert fit_classifier, 'this quantifier needs to fit the classifier!'
        self.classifier.fit(*data.Xy)
        return self

    # in general, we would need to implement the method quantify(self, instances) but, since this method is of
    # type aggregative, we can simply implement the method aggregate, which has the following interface
    def aggregate(self, classif_predictions: np.ndarray):
        # the posterior probabilities have already been generated by the quantify method; we only need to
        # specify what to do with them
        positive_probabilities = classif_predictions[:, 1]
        crisp_decisions = positive_probabilities > self.alpha
        pos_prev = crisp_decisions.mean()
        neg_prev = 1-pos_prev
        return np.asarray([neg_prev, pos_prev])


if __name__ == '__main__':

    qp.environ['SAMPLE_SIZE'] = 100

    # define an instance of our custom quantifier
    quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)

    # load the IMDb dataset
    train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test

    # model selection
    # let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier
    train, val = train.split_stratified(train_prop=0.75)
    param_grid = {
        'alpha': np.linspace(0, 1, 11),         # quantifier-dependent hyperparameter
        'classifier__C': np.logspace(-2, 2, 5)  # classifier-dependent hyperparameter
    }
    quantifier = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=True).fit(train)

    # evaluation
    mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae')

    print(f'MAE = {mae:.4f}')

    # final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
    # on an hyperparameter alpha for binarizing the posterior probabilities. A much better way for fulfilling this
    # goal would be to calibrate the classifier (LogisticRegression is already reasonably well calibrated) and then
    # simply cut at 0.5.