from sklearn.svm import SVC

from sklearn.model_selection import KFold

from os.path import abspath

from os.path import split as pathsplit

# absolute path of this file

PATH_THIS_FILE = pathsplit(abspath(__file__))[0]

#################### SimDeep variable ##################

USE_R_PACKAGES_FOR_SURVIVAL = False

NB_CLUSTERS = 2 # Number of clusters

CLUSTER_METHOD = 'coxPHMixture' # possible choice: ['mixture', 'kmeans', 'coxPH', 'coxPHMixture']

CLUSTER_EVAL_METHOD = 'silhouette' # possible choice: ['bic', 'silhouette', 'calinski']

CLASSIFIER_TYPE = 'svm'

NODES_SELECTION = 'Cox-PH' # possible choice: ['Cox-PH', 'C-index']

CLASSIFICATION_METHOD = 'ALL_FEATURES' # ['ALL_FEATURES', 'SURVIVAL_FEATURES']

FILL_UNKOWN_FEATURE_WITH_0 = True

USE_AUTOENCODERS = True # Construct autoencoders rather than using all the features

FEATURE_SURV_ANALYSIS = True # Refine feature with survival analysis

# Number of top features selected for classification

# Apply only when CLASSIFICATION_METHOD == 'ALL_FEATURES'

NB_SELECTED_FEATURES = 50

CLUSTER_ARRAY = []

PVALUE_THRESHOLD = 0.01 # Threshold for survival significance to set a node as valid

CINDEX_THRESHOLD = 0.65 # experimental

NB_THREADS_COXPH = 10

STACK_MULTI_OMIC = False

#### Boosting values

NB_ITER = 10 # boosting iteration

NB_THREADS= 5 # number of simdeep instance launched in parallel

NB_FOLDS = 5 # for each instance, the original dataset is split in folds and one fold is left

CLASS_SELECTION = 'mean' # mean or max: the method used to select the final class, according to class probas

MODEL_THRES = 0.05 # Cox-PH p-value threshold to reject a model for DeepProg Boosting module

#### SimDeep analysis

# save fitted models

SAVE_FITTED_MODELS = False

# load existing models if founds

LOAD_EXISTING_MODELS = False

# Which omics to use for clustering. If empty, then all the available omics will be used

CLUSTERING_OMICS = []

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

#################### Paths to data file ################

# path to the folder containing the data

PROJECT_NAME = 'test_dummy_dataset'

PATH_DATA = PATH_THIS_FILE + "/../examples/data/"

# PATH_DATA = "/home/opoirion/data/survival_analysis_multiple/"

# name of the tsv file containing the survival data of the training set

SURVIVAL_TSV = 'survival_dummy.tsv'

# name of the tsv file containing the survival data of the test set

SURVIVAL_TSV_TEST = 'survival_test_dummy.tsv'

# True if

USE_INPUT_TRANSPOSE = False

ENTREZ_TO_ENSG_FILE = PATH_THIS_FILE + '/../data/entrez2ensg.tsv'

# Field from the survival tsv file

SURVIVAL_FLAG = {'patient_id': 'barcode',

                  'survival': 'days',

                 'event': 'recurrence'}

# dict('data type', 'name of the tsv file which are inside PATH_DATA')

# These data will be stacked together to build the autoencoder

TRAINING_TSV = {

    'GE': 'rna_dummy.tsv',

    'MIR': 'mir_dummy.tsv',

    'METH': 'meth_dummy.tsv',

}

TEST_TSV = {

    'MIR': 'mir_test_dummy.tsv',

}

DEFAULTSEP = '\t'

SEPARATOR = {

    '0717_methyl_cnv_inter_matrix.tsv' : ' ',

    '0717_expr_methyl_inter_matrix.tsv': ' ',

    '0717_expr_cnv_inter_matrix.tsv': ' ',

    }

# Path where to save load the Keras models

PATH_TO_SAVE_MODEL = './'

# Path to generate png images

PATH_RESULTS = './'

######## Cross-validation on the training set ############

CROSS_VALIDATION_INSTANCE = KFold(n_splits=5, shuffle=True, random_state=1)

TEST_FOLD = 0

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

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

##################### NORMALIZATION PROCEDURE ###########

## Normalize before the autoencoder construction ########

NORMALIZATION = {

    'NB_FEATURES_TO_KEEP': 100, # variance selection features. 0 is all the features

    'TRAIN_MIN_MAX': False,

    'TRAIN_ROBUST_SCALE': False,

    'TRAIN_ROBUST_SCALE_TWO_WAY': False,

    'TRAIN_MAD_SCALE': False,

    'TRAIN_QUANTILE_TRANSFORM': False,

    'TRAIN_NORM_SCALE': False,

    'TRAIN_RANK_NORM': True,

    'TRAIN_CORR_REDUCTION': True,

    'TRAIN_CORR_RANK_NORM': True,

}

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

##################### Autoencoder Variable ##############

# Dimensions of the intermediate layers before and after the middle hidden layer

# if LEVEL_DIMS == [500, 250] then there will be two hidden layers with 500 and 250 nodes

# before and after the hidden middle layer (5 hidden layers)

# if LEVEL_DIMS = [], then the autoencoder will have only one hidden layer

LEVEL_DIMS_IN = ()

LEVEL_DIMS_OUT = ()

# Number of nodes in the middle hidden layer

# (i.e. the new dimensions of the transformed data)

NEW_DIM = 100

# Percentage of edges being dropout at each training iteration (None for no dropout)

DROPOUT = 0.5

# L2 Regularization constant on the node activity

ACT_REG = False

# L1 Regularization constant on the weight

W_REG = False

# Fraction of the dataset to be used as test set when building the autoencoder

DATA_SPLIT = None

# activation function

ACTIVATION = 'tanh'

# Number of epoch

EPOCHS = 10

# Loss function to minimize

LOSS = 'binary_crossentropy'

# Optimizer (sgd for Stochastic Gradient Descent)

OPTIMIZER = 'adam'

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

################## CLASSIFIER ##########################

# Variables used to perform the supervized classification procedure

# to assign labels to the test set

MIXTURE_PARAMS = {

    'covariance_type': 'diag',

    'max_iter': 1000,

    'n_init': 100

    }

# Hyper parameters used to perform the grid search to find the best classifier

HYPER_PARAMETERS = [

    {'kernel': ['rbf'],

     'class_weight': [None, 'balanced'],

     'gamma': [0.1, 0.05, 0.01, 0.005, 0.001, 0.0001, 0.00001],

     'C': [1000, 750, 500, 250, 100, 50, 10, 5, 1, 0.1],

     'max_iter':[10000],

     },

    {'kernel': ['linear'],

     'class_weight': [None, 'balanced'],

     'C': [1000, 750, 500, 250, 100, 50, 10, 5, 1, 0.1],

     'max_iter':[10000],

     }

]

# grid search classifier using Support Vector Machine Classifier (SVC)

CLASSIFIER = SVC

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

#################### Other variables #####################

SEED = 2020 # for experiment reproducibility (if set to an integer)

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