import argparse
# PATHs

id='8'

xp_name = 'strept_pneumo_31'
#xp_name = 'esche_amox_31'
#xp_name='acineto_imip_20'

mode='serv' #can be ['local', 'serv', 'laptop']

dtype='sparse' #can be ['df','sparse']


if mode == 'serv':
    pathtoxp = '/mnt/cbib/MAGITICS_Yvan/experiments_kmer_count/'
    pathtodata='/scratch/MAGITICS_data/'
#    testdir='Pseudomonas_aeruginosa/levofloxacin/test/test'
#    testdir='Escherichia_coli/test/test'
#    data='Escherichia_coli/traindata/'
#    data = 'Pseudomonas_aeruginosa/levofloxacin/traindata/'
    data = 'Streptococcus_pneumoniae/datajie/'
    testdir = 'Streptococcus_pneumoniae/testjie/'
    #data='Acinetobacter_baumanii/traindata/'
    #testdir='Acinetobacter_baumanii/test/test/'
elif mode == 'local':
    pathtoxp = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    pathtodata='/home/ylucas/toydata_pseudomonas_levofloxacin/'
    data='traindatabis'
    testdir='test/test'
elif mode == 'laptop':
    pathtoxp='/home/ylucas/Bureau/expe_postdoc/xp'
    pathtodata='/home/ylucas/Bureau/expe_postdoc/data_postdoc/'
    data= ''
    testdir='test'

# Kmer extraction parameters

kmer_count = 1  # 1: kmer count, 0: presence/absence
len_kmers = 20
MIC=True

min_abundance = 3 #not used atm
kmer_count=1 #1: kmer count, 0: presence/absence

len_kmers=20
# Learning parameters
model = 'gradient'  # can be ['rf','SCM', 'gradient', 'Ada']

rf_grid = {'max_features': ['sqrt', 'log2'],
           'max_depth': [4, 8]}

SCM_grid = {'p': [1, 10], 'max_rules': [1, 3 ,10], 'model_type':['conjunction','disjunction']}

gradient_grid = {'max_depth': [1, 2, 4],
                 'n_estimators': [10, 6,4, 2] }

ada_grid =  {'n_estimators': [ 5, 10, 20]}


pruning_tresh=0.9

def get_lenkmers():
    parser=argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg=parser.parse_args()
    return arg.len_kmers

len_kmers= 20 #int(get_lenkmers())

id='MICkmers20penicillin'#+str(int(get_lenkmers()))

44c443dd0968a99dcc44bc1501e5568c13a8700f
<mask token>


def get_lenkmers():
    parser = argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg = parser.parse_args()
    return arg.len_kmers


<mask token>

<mask token>
if mode == 'serv':
    pathtoxp = '/mnt/cbib/MAGITICS_Yvan/experiments_kmer_count/'
    pathtodata = '/scratch/MAGITICS_data/'
    data = 'Streptococcus_pneumoniae/datajie/'
    testdir = 'Streptococcus_pneumoniae/testjie/'
elif mode == 'local':
    pathtoxp = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    pathtodata = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    data = 'traindatabis'
    testdir = 'test/test'
elif mode == 'laptop':
    pathtoxp = '/home/ylucas/Bureau/expe_postdoc/xp'
    pathtodata = '/home/ylucas/Bureau/expe_postdoc/data_postdoc/'
    data = ''
    testdir = 'test'
<mask token>


def get_lenkmers():
    parser = argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg = parser.parse_args()
    return arg.len_kmers


<mask token>

<mask token>
id = '8'
xp_name = 'strept_pneumo_31'
mode = 'serv'
dtype = 'sparse'
if mode == 'serv':
    pathtoxp = '/mnt/cbib/MAGITICS_Yvan/experiments_kmer_count/'
    pathtodata = '/scratch/MAGITICS_data/'
    data = 'Streptococcus_pneumoniae/datajie/'
    testdir = 'Streptococcus_pneumoniae/testjie/'
elif mode == 'local':
    pathtoxp = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    pathtodata = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    data = 'traindatabis'
    testdir = 'test/test'
elif mode == 'laptop':
    pathtoxp = '/home/ylucas/Bureau/expe_postdoc/xp'
    pathtodata = '/home/ylucas/Bureau/expe_postdoc/data_postdoc/'
    data = ''
    testdir = 'test'
kmer_count = 1
len_kmers = 20
MIC = True
min_abundance = 3
kmer_count = 1
len_kmers = 20
model = 'gradient'
rf_grid = {'max_features': ['sqrt', 'log2'], 'max_depth': [4, 8]}
SCM_grid = {'p': [1, 10], 'max_rules': [1, 3, 10], 'model_type': [
    'conjunction', 'disjunction']}
gradient_grid = {'max_depth': [1, 2, 4], 'n_estimators': [10, 6, 4, 2]}
ada_grid = {'n_estimators': [5, 10, 20]}
pruning_tresh = 0.9


def get_lenkmers():
    parser = argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg = parser.parse_args()
    return arg.len_kmers


len_kmers = 20
id = 'MICkmers20penicillin'

import argparse
id = '8'
xp_name = 'strept_pneumo_31'
mode = 'serv'
dtype = 'sparse'
if mode == 'serv':
    pathtoxp = '/mnt/cbib/MAGITICS_Yvan/experiments_kmer_count/'
    pathtodata = '/scratch/MAGITICS_data/'
    data = 'Streptococcus_pneumoniae/datajie/'
    testdir = 'Streptococcus_pneumoniae/testjie/'
elif mode == 'local':
    pathtoxp = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    pathtodata = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    data = 'traindatabis'
    testdir = 'test/test'
elif mode == 'laptop':
    pathtoxp = '/home/ylucas/Bureau/expe_postdoc/xp'
    pathtodata = '/home/ylucas/Bureau/expe_postdoc/data_postdoc/'
    data = ''
    testdir = 'test'
kmer_count = 1
len_kmers = 20
MIC = True
min_abundance = 3
kmer_count = 1
len_kmers = 20
model = 'gradient'
rf_grid = {'max_features': ['sqrt', 'log2'], 'max_depth': [4, 8]}
SCM_grid = {'p': [1, 10], 'max_rules': [1, 3, 10], 'model_type': [
    'conjunction', 'disjunction']}
gradient_grid = {'max_depth': [1, 2, 4], 'n_estimators': [10, 6, 4, 2]}
ada_grid = {'n_estimators': [5, 10, 20]}
pruning_tresh = 0.9


def get_lenkmers():
    parser = argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg = parser.parse_args()
    return arg.len_kmers


len_kmers = 20
id = 'MICkmers20penicillin'

import argparse
# PATHs

id='8'

xp_name = 'strept_pneumo_31'
#xp_name = 'esche_amox_31'
#xp_name='acineto_imip_20'

mode='serv' #can be ['local', 'serv', 'laptop']

dtype='sparse' #can be ['df','sparse']


if mode == 'serv':
    pathtoxp = '/mnt/cbib/MAGITICS_Yvan/experiments_kmer_count/'
    pathtodata='/scratch/MAGITICS_data/'
#    testdir='Pseudomonas_aeruginosa/levofloxacin/test/test'
#    testdir='Escherichia_coli/test/test'
#    data='Escherichia_coli/traindata/'
#    data = 'Pseudomonas_aeruginosa/levofloxacin/traindata/'
    data = 'Streptococcus_pneumoniae/datajie/'
    testdir = 'Streptococcus_pneumoniae/testjie/'
    #data='Acinetobacter_baumanii/traindata/'
    #testdir='Acinetobacter_baumanii/test/test/'
elif mode == 'local':
    pathtoxp = '/home/ylucas/toydata_pseudomonas_levofloxacin/'
    pathtodata='/home/ylucas/toydata_pseudomonas_levofloxacin/'
    data='traindatabis'
    testdir='test/test'
elif mode == 'laptop':
    pathtoxp='/home/ylucas/Bureau/expe_postdoc/xp'
    pathtodata='/home/ylucas/Bureau/expe_postdoc/data_postdoc/'
    data= ''
    testdir='test'

# Kmer extraction parameters

kmer_count = 1  # 1: kmer count, 0: presence/absence
len_kmers = 20
MIC=True

min_abundance = 3 #not used atm
kmer_count=1 #1: kmer count, 0: presence/absence

len_kmers=20
# Learning parameters
model = 'gradient'  # can be ['rf','SCM', 'gradient', 'Ada']

rf_grid = {'max_features': ['sqrt', 'log2'],
           'max_depth': [4, 8]}

SCM_grid = {'p': [1, 10], 'max_rules': [1, 3 ,10], 'model_type':['conjunction','disjunction']}

gradient_grid = {'max_depth': [1, 2, 4],
                 'n_estimators': [10, 6,4, 2] }

ada_grid =  {'n_estimators': [ 5, 10, 20]}


pruning_tresh=0.9

def get_lenkmers():
    parser=argparse.ArgumentParser()
    parser.add_argument('--len_kmers', type=int, default=31)
    arg=parser.parse_args()
    return arg.len_kmers

len_kmers= 20 #int(get_lenkmers())

id='MICkmers20penicillin'#+str(int(get_lenkmers()))
