from HMF.code.cross_validation.cross_validation_hmf import CrossValidation
from HMF.drug_sensitivity.load_dataset import load_data_without_empty, load_data_filter
import numpy
''' Load datasets '''
location = project_location+"HMF/drug_sensitivity/data/overlap/"
location_data = location+"data_row_01/"
location_features_drugs = location+"features_drugs/"
location_features_cell_lines = location+"features_cell_lines/"
location_kernels = location+"kernels_features/"
R_ccle_ec, M_ccle_ec, cell_lines, drugs = load_data_without_empty(location_data+"ccle_ec50_row_01.txt")
R_ctrp, M_ctrp = load_data_filter(location_data+"ctrp_ec50_row_01.txt",cell_lines,drugs)
R_gdsc, M_gdsc = load_data_filter(location_data+"gdsc_ic50_row_01.txt",cell_lines,drugs)
R_ccle_ic, M_ccle_ic = load_data_filter(location_data+"ccle_ic50_row_01.txt",cell_lines,drugs)
''' Settings HMF '''
iterations, burn_in, thinning = 200, 150, 2 # 500, 400, 2
no_folds = 10
hyperparameters = {
'alphatau' : 1.,
'betatau' : 1.,
'alpha0' : 0.001,
'beta0' : 0.001,
'lambdaF' : 0.1,
'lambdaG' : 0.1,
from HMF.code.models.nmf_np import nmf_np
from HMF.code.cross_validation.multiple_nmf_nested_matrix_cross_validation import MultipleNMFNestedCrossValidation
from HMF.drug_sensitivity.load_dataset import load_data_without_empty, load_data_filter
import numpy, random
''' Load datasets '''
location = project_location + "HMF/drug_sensitivity/data/overlap/"
location_data = location + "data_row_01/"
location_features_drugs = location + "features_drugs/"
location_features_cell_lines = location + "features_cell_lines/"
location_kernels = location + "kernels_features/"
''' Concatenate the datasets by ROWS. We remove the columns of the other datasets '''
R_ccle_ec, M_ccle_ec, cell_lines, drugs = load_data_without_empty(
location_data + "ccle_ec50_row_01.txt")
R_ctrp, M_ctrp = load_data_filter(location_data + "ctrp_ec50_row_01.txt",
rows=cell_lines,
columns=None)
R_gdsc, M_gdsc = load_data_filter(location_data + "gdsc_ic50_row_01.txt",
rows=cell_lines,
columns=None)
R_ccle_ic, M_ccle_ic = load_data_filter(location_data + "ccle_ic50_row_01.txt",
rows=cell_lines,
columns=None)
R_concat = numpy.concatenate((R_ccle_ec, R_gdsc, R_ctrp, R_ccle_ic),
axis=1) #columns
M_concat = numpy.concatenate((M_ccle_ec, M_gdsc, M_ctrp, M_ccle_ic),
axis=1) #columns
_, no_columns = R_ccle_ec.shape
''' Remove entirely empty columns, due to the other three datasets that we concatenate '''
from HMF.code.models.nmf_np import nmf_np
from HMF.code.cross_validation.multiple_nmf_nested_matrix_cross_validation import MultipleNMFNestedCrossValidation
from HMF.drug_sensitivity.load_dataset import load_data_without_empty, load_data_filter
import numpy, random
''' Load datasets '''
location = project_location+"HMF/drug_sensitivity/data/overlap/"
location_data = location+"data_row_01/"
location_features_drugs = location+"features_drugs/"
location_features_cell_lines = location+"features_cell_lines/"
location_kernels = location+"kernels_features/"
''' Concatenate the datasets by ROWS. We remove the columns of the other datasets '''
R_gdsc, M_gdsc, cell_lines, drugs = load_data_without_empty(location_data+"gdsc_ic50_row_01.txt")
R_ctrp, M_ctrp = load_data_filter(location_data+"ctrp_ec50_row_01.txt",rows=None,columns=drugs)
R_ccle_ec, M_ccle_ec = load_data_filter(location_data+"ccle_ec50_row_01.txt",rows=None,columns=drugs)
R_ccle_ic, M_ccle_ic = load_data_filter(location_data+"ccle_ic50_row_01.txt",rows=None,columns=drugs)
R_concat = numpy.concatenate((R_gdsc,R_ctrp,R_ccle_ec,R_ccle_ic),axis=0) #rows
M_concat = numpy.concatenate((M_gdsc,M_ctrp,M_ccle_ec,M_ccle_ic),axis=0) #rows
no_rows, _ = R_gdsc.shape
''' Remove entirely empty rows, due to the other three datasets that we concatenate '''
def remove_empty_rows(R,M):
new_R, new_M = [], []
for i,sum_row in enumerate(M.sum(axis=1)):
if sum_row > 0:
new_R.append(R[i])
new_M.append(M[i])
return numpy.array(new_R), numpy.array(new_M)
''' Model settings ''' n_estimators = 100 # number of trees max_depth = None # until what depth of feature splits we go ''' Load datasets ''' location = project_location+"HMF/drug_sensitivity/data/overlap/" location_data = location+"data_row_01/" location_features_drugs = location+"features_drugs/" location_features_cell_lines = location+"features_cell_lines/" location_kernels = location+"kernels_features/" R_main, M_main, cell_lines, drugs = load_data_without_empty(location_data+"gdsc_ic50_row_01.txt") R_cnv, M_cnv = load_data_filter(location_features_cell_lines+"cnv.txt", cell_lines) #R_cnv_std, M_cnv_std = load_data_filter(location_features_cell_lines+"cnv_std.txt", cell_lines) R_mutation, M_mutation = load_data_filter(location_features_cell_lines+"mutation.txt", cell_lines) #R_ge, M_ge = load_data_filter(location_features_cell_lines+"gene_expression.txt", cell_lines) #R_ge_std, M_ge_std = load_data_filter(location_features_cell_lines+"gene_expression_std.txt", cell_lines) R_fp, M_fp = load_data_filter(location_features_drugs+"drug_fingerprints.txt", drugs) R_targets, M_targets = load_data_filter(location_features_drugs+"drug_targets.txt", drugs) R_1d2d, M_1d2d = load_data_filter(location_features_drugs+"drug_1d2d.txt", drugs) #R_1d2d_std, M_1d2d_std = load_data_filter(location_features_drugs+"drug_1d2d_std.txt", drugs) features_drugs = [R_fp, R_targets, R_1d2d] features_cell_lines = [R_cnv, R_mutation] ''' Split the mask M into folds '''
from HMF.code.cross_validation.cross_validation_hmf import CrossValidation
from HMF.drug_sensitivity.load_dataset import load_data_without_empty, load_data_filter
import numpy, random
''' Load datasets '''
location = project_location + "HMF/drug_sensitivity/data/overlap/"
location_data = location + "data_row_01/"
location_features_drugs = location + "features_drugs/"
location_features_cell_lines = location + "features_cell_lines/"
location_kernels = location + "kernels_features/"
R_gdsc, M_gdsc, cell_lines, drugs = load_data_without_empty(
location_data + "gdsc_ic50_row_01.txt")
C_cnv_std, M_cnv_std = load_data_filter(location_kernels + "cnv_std.txt",
cell_lines, cell_lines)
C_ge_std, M_ge_std = load_data_filter(
location_kernels + "gene_expression_std.txt", cell_lines, cell_lines)
C_mutation, M_mutation = load_data_filter(location_kernels + "mutation.txt",
cell_lines, cell_lines)
C_1d2d_std, M_1d2d_std = load_data_filter(
location_kernels + "drug_1d2d_std.txt", drugs, drugs)
C_fp, M_fp = load_data_filter(location_kernels + "drug_fingerprints.txt",
drugs, drugs)
C_targets, M_targets = load_data_filter(location_kernels + "drug_targets.txt",
drugs, drugs)
''' Settings HMF '''
iterations, burn_in, thinning = 100, 80, 2
no_folds = 10
