import unittest

import warnings

import numpy as np

from simdeep.config import ACTIVATION

from simdeep.config import OPTIMIZER

from simdeep.config import LOSS

from os.path import abspath

from os.path import split

from os.path import isfile

from os.path import isdir

from os import remove

from shutil import rmtree

class TestPackage(unittest.TestCase):

    """ """

    def test_1_coxph_function(self):

        """test if the coxph function works """

        from simdeep.coxph_from_r import coxph

        isdead = [0, 1, 1, 1, 0, 1, 0, 0, 1, 0]

        nbdays = [24, 10, 25, 50, 14, 10 ,100, 10, 50, 10]

        values = [0, 1, 1, 0 , 1, 2, 0, 1, 0, 0]

        pvalue = coxph(values, isdead, nbdays, isfactor=True)

        self.assertTrue(isinstance(pvalue, float))

        self.assertTrue(pvalue < 0.05)

    def test_4_keras_model_instantiation(self):

        """

        test if keras can be loaded and if that a model

        can be instanciated

        """

        from keras.models import Sequential

        from keras.layers import Dense

        dummy_model = Sequential()

        dummy_model.add(Dense(10, input_dim=20,

                                   activation=ACTIVATION))

        dummy_model.compile(

            optimizer=OPTIMIZER, loss=LOSS)

        Xmat = np.random.random((50,20))

        Ymat = np.random.random((50,10))

        dummy_model.fit(

            x=Xmat,

            y=Ymat)

    def test_5_one_simdeep_instance(self):

        """

        test one simdeep instance

        """

        from simdeep.simdeep_analysis import SimDeep

        from simdeep.extract_data import LoadData

        PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])

        TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv', 'MIR': 'mir_dummy.tsv'}

        SURVIVAL_TSV = 'survival_dummy.tsv'

        PROJECT_NAME = 'TestProject'

        EPOCHS = 3

        deep_model_additional_args = {

        "epochs":EPOCHS, "seed":4}

        dataset = LoadData(path_data=PATH_DATA,

                       survival_tsv=SURVIVAL_TSV,

                       training_tsv=TRAINING_TSV)

        simdeep = SimDeep(dataset=dataset,

                          project_name=PROJECT_NAME,

                          path_results="{0}/{1}".format(PATH_DATA, PROJECT_NAME),

                          deep_model_additional_args=deep_model_additional_args,

        )

        simdeep.load_training_dataset()

        simdeep.fit()

        simdeep.predict_labels_on_full_dataset()

        simdeep.predict_labels_on_test_fold()

        simdeep.load_new_test_dataset(

            tsv_dict={'RNA': 'rna_test_dummy.tsv'},

            fname_key='dummy',

            path_survival_file='survival_test_dummy.tsv')

        simdeep.predict_labels_on_test_dataset()

        path_fig = '{0}/{1}/{1}_KM_plot_training_dataset.pdf'.format(PATH_DATA, PROJECT_NAME)

        print('#### asserting file: {0} exists'.format(path_fig))

        self.assertTrue(isfile(path_fig))

        from glob import glob

        for fil in glob('{0}/{1}*'.format(PATH_DATA, PROJECT_NAME)):

            if isfile(fil):

                remove(fil)

            elif isdir(fil):

                rmtree(fil)

    def test_6_simdeep_boosting(self):

        """

        test simdeep boosting

        """

        from simdeep.simdeep_boosting import SimDeepBoosting

        PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])

        TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv', 'MIR': 'mir_dummy.tsv'}

        SURVIVAL_TSV = 'survival_dummy.tsv'

        PROJECT_NAME = 'TestProject'

        EPOCHS = 3

        SEED = 3

        nb_it = 3

        nb_threads = 2

        boosting = SimDeepBoosting(

            nb_threads=nb_threads,

            nb_it=nb_it,

            survival_tsv=SURVIVAL_TSV,

            training_tsv=TRAINING_TSV,

            path_data=PATH_DATA,

            project_name=PROJECT_NAME,

            path_results=PATH_DATA,

            epochs=EPOCHS,

            normalization={'TRAIN_CORR_REDUCTION':True},

            seed=SEED)

        boosting.partial_fit()

        boosting.predict_labels_on_full_dataset()

        boosting.compute_clusters_consistency_for_full_labels()

        boosting.evalutate_cluster_performance()

        boosting.collect_cindex_for_test_fold()

        boosting.collect_cindex_for_full_dataset()

        boosting.load_new_test_dataset(

            tsv_dict={'RNA': 'rna_test_dummy.tsv'},

            fname_key='dummy',

            path_survival_file='survival_test_dummy.tsv',

            normalization={'TRAIN_NORM_SCALE':True},

        )

        boosting.predict_labels_on_test_dataset()

        boosting.predict_labels_on_test_dataset()

        boosting.compute_c_indexes_for_test_dataset()

        boosting.compute_clusters_consistency_for_test_labels()

        from glob import glob

        for fil in glob('{0}/{1}*'.format(PATH_DATA, PROJECT_NAME)):

            if isfile(fil):

                remove(fil)

            elif isdir(fil):

                rmtree(fil)

if __name__ == "__main__":

    unittest.main()