import os

os.environ['PYTHONHASHSEED']=str(2020)

import random

random.seed(2020)

from os.path import abspath

from os.path import split

from simdeep.simdeep_boosting import SimDeepBoosting

from simdeep.simdeep_utils import save_model

from simdeep.simdeep_utils import load_model

def test_instance():

    """

    example of SimDeepBoosting

    """

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

    #Input file

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

    SURVIVAL_TSV = 'survival_dummy.tsv'

    PROJECT_NAME = 'TestProject'

    SEED = 3

    nb_it = 2 # Number of models to be built

    nb_threads = 2 # Number of processes to be used to fit individual survival models

    ################ AUTOENCODER PARAMETERS ################

    EPOCHS = 10

    ## Additional parameters for the autoencoders can be defined, see config.py file for details

    # LEVEL_DIMS_IN = [250]

    # LEVEL_DIMS_OUT = [250]

    # LOSS = 'binary_crossentropy'

    # OPTIMIZER = 'adam'

    # ACT_REG = 0

    # W_REG = 0

    # DROPOUT = 0.5

    # DATA_SPLIT = 0

    # ACTIVATION = 'tanh'

    #########################################################

    ################ ADDITIONAL PARAMETERS ##################

    # PATH_TO_SAVE_MODEL = '/home/username/deepprog'

    # PVALUE_THRESHOLD = 0.01

    # NB_SELECTED_FEATURES = 10

    # STACK_MULTI_OMIC = False

    #########################################################

    from sklearn.preprocessing import RobustScaler

    norm = {

            'CUSTOM': RobustScaler,

    }

    boosting = SimDeepBoosting(

        nb_threads=nb_threads,

        nb_it=nb_it,

        split_n_fold=3,

        survival_tsv=SURVIVAL_TSV,

        training_tsv=TRAINING_TSV,

        path_data=PATH_DATA,

        project_name=PROJECT_NAME,

        path_results=PATH_DATA,

        epochs=EPOCHS,

        seed=SEED,

        normalization=norm,

        cluster_method='mixture',

        use_autoencoders=True,

        feature_surv_analysis=True,

        distribute=False

        # stack_multi_omic=STACK_MULTI_OMIC,

        # level_dims_in=LEVEL_DIMS_IN,

        # level_dims_out=LEVEL_DIMS_OUT,

        # loss=LOSS,

        # optimizer=OPTIMIZER,

        # act_reg=ACT_REG,

        # w_reg=W_REG,

        # dropout=DROPOUT,

        # data_split=DATA_SPLIT,

        # activation=ACTIVATION,

        # path_to_save_model=PATH_TO_SAVE_MODEL,

        # pvalue_threshold=PVALUE_THRESHOLD,

        # nb_selected_features=NB_SELECTED_FEATURES,

    )

    boosting.fit()

    save_model(boosting, "./test_saved_model")

    del boosting

    boosting = load_model("TestProject", "./test_saved_model")

    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.compute_feature_scores_per_cluster()

    boosting.write_feature_score_per_cluster()

    boosting.collect_number_of_features_per_omic()

    boosting.compute_pvalue_for_merged_test_fold()

    boosting.load_new_test_dataset(

        {'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training

        'dummy', # Name of the test test to be used

        'survival_dummy.tsv', # Survival file of the test set Optional

    )

    boosting.predict_labels_on_test_dataset()

    boosting.compute_c_indexes_for_test_dataset()

    boosting.compute_clusters_consistency_for_test_labels()

    boosting.load_new_test_dataset(

        {'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.

        'dummy_METH', # Name of the second test test

        'survival_dummy.tsv', # Survival file of the test set Optional

    )

    boosting.predict_labels_on_test_dataset()

    boosting.compute_c_indexes_for_test_dataset()

    boosting.compute_clusters_consistency_for_test_labels()

if __name__ == '__main__':

    test_instance()