from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.svm import LinearSVC
import quapy as qp
import quapy.functional as F
import sys
import numpy as np
from classification.methods import PCALR
from classification.neural import NeuralClassifierTrainer, CNNnet
from quapy.model_selection import GridSearchQ



#qp.datasets.fetch_UCIDataset('acute.b', verbose=True)

#sys.exit(0)
qp.environ['SAMPLE_SIZE'] = 500
#param_grid = {'C': np.logspace(-3,3,7), 'class_weight': ['balanced', None]}
param_grid = {'C': np.logspace(0,3,4), 'class_weight': ['balanced']}
max_evaluations = 5000

sample_size = qp.environ['SAMPLE_SIZE']
binary = True
svmperf_home = './svm_perf_quantification'

if binary:
    dataset = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
    #qp.data.preprocessing.index(dataset, inplace=True)

else:
    dataset = qp.datasets.fetch_twitter('hcr', for_model_selection=False, min_df=10, pickle=True)
    dataset.training = dataset.training.sampling(sample_size, 0.2, 0.5, 0.3)

print(f'dataset loaded: #training={len(dataset.training)} #test={len(dataset.test)}')


# training a quantifier
# learner = LogisticRegression(max_iter=1000)
#model = qp.method.aggregative.ClassifyAndCount(learner)
# model = qp.method.aggregative.AdjustedClassifyAndCount(learner)
# model = qp.method.aggregative.ProbabilisticClassifyAndCount(learner)
# model = qp.method.aggregative.ProbabilisticAdjustedClassifyAndCount(learner)
# model = qp.method.aggregative.HellingerDistanceY(learner)
# model = qp.method.aggregative.ExpectationMaximizationQuantifier(learner)
# model = qp.method.aggregative.ExplicitLossMinimisationBinary(svmperf_home, loss='q', C=100)
# model = qp.method.aggregative.SVMQ(svmperf_home, C=1)

#learner = PCALR()
#learner = NeuralClassifierTrainer(CNNnet(dataset.vocabulary_size, dataset.n_classes))
#print(learner.get_params())
#model = qp.method.meta.QuaNet(learner, sample_size, device='cpu')

#learner = GridSearchCV(LogisticRegression(max_iter=1000), param_grid=param_grid, n_jobs=-1, verbose=1)
learner = LogisticRegression(max_iter=1000)
model = qp.method.meta.ECC(learner, size=20, red_size=10, param_grid=None, optim=None, policy='ds')
#model = qp.method.meta.EHDy(learner, param_grid=param_grid, optim='mae',
#                           sample_size=sample_size, eval_budget=max_evaluations//10, n_jobs=-1)
#model = qp.method.aggregative.ClassifyAndCount(learner)


#if qp.isbinary(model) and not qp.isbinary(dataset):
#    model = qp.method.aggregative.OneVsAll(model)


# Model fit and Evaluation on the test data
# ----------------------------------------------------------------------------

print(f'fitting model {model.__class__.__name__}')
#train, val = dataset.training.split_stratified(0.6)
#model.fit(train, val_split=val)
model.fit(dataset.training)
#for i,e in enumerate(model.ensemble):
    #print(i, e.learner.best_estimator_)
#    print(i, e.best_model_.learner)


# estimating class prevalences
print('quantifying')
prevalences_estim = model.quantify(dataset.test.instances)
prevalences_true  = dataset.test.prevalence()

# evaluation (one single prediction)
error = qp.error.mae(prevalences_true, prevalences_estim)

print(f'Evaluation in test (1 eval)')
print(f'true prevalence {F.strprev(prevalences_true)}')
print(f'estim prevalence {F.strprev(prevalences_estim)}')
print(f'mae={error:.3f}')


# Model fit and Evaluation according to the artificial sampling protocol
# ----------------------------------------------------------------------------


n_prevpoints = F.get_nprevpoints_approximation(combinations_budget=max_evaluations, n_classes=dataset.n_classes)
n_evaluations = F.num_prevalence_combinations(n_prevpoints, dataset.n_classes)
print(f'the prevalence interval [0,1] will be split in {n_prevpoints} prevalence points for each class, so that\n'
      f'the requested maximum number of sample evaluations ({max_evaluations}) is not exceeded.\n'
      f'For the {dataset.n_classes} classes this dataset has, this will yield a total of {n_evaluations} evaluations.')

true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(model, dataset.test, sample_size, n_prevpoints)

#qp.error.SAMPLE_SIZE = sample_size
print(f'Evaluation according to the artificial sampling protocol ({len(true_prev)} evals)')
for error in qp.error.QUANTIFICATION_ERROR:
    score = error(true_prev, estim_prev)
    print(f'{error.__name__}={score:.5f}')


# Model selection and Evaluation according to the artificial sampling protocol
# ----------------------------------------------------------------------------
sys.exit(0)


model_selection = GridSearchQ(model,
                              param_grid=param_grid,
                              sample_size=sample_size,
                              eval_budget=max_evaluations//10,
                              error='mae',
                              refit=True,
                              verbose=True)

model = model_selection.fit(dataset.training, validation=0.3)
#model = model_selection.fit(train, validation=val)
print(f'Model selection: best_params = {model_selection.best_params_}')
print(f'param scores:')
for params, score in model_selection.param_scores_.items():
    print(f'\t{params}: {score:.5f}')

true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(model, dataset.test, sample_size, n_prevpoints)

print(f'After model selection: Evaluation according to the artificial sampling protocol ({len(true_prev)} evals)')
for error in qp.error.QUANTIFICATION_ERROR:
    score = error(true_prev, estim_prev)
    print(f'{error.__name__}={score:.5f}')