metrics = ['MSE', 'R^2', 'Rp']
''' Load data '''
location = project_location+"DI_MMTF/data/datasets_drug_sensitivity/overlap/"
location_data = location+"data_row_01/"
R, M_original, _, _ = load_data_without_empty(location_data+"ctrp_ec50_row_01.txt")
#''' Seed all of the methods the same '''
#numpy.random.seed(0)
#random.seed(0)
''' Generate matrices M - one list of (M_train,M_test)'s for each fraction '''
M_attempts = 10000
all_Ms_train_test = [
[try_generate_M_from_M(M=M_original,fraction=fraction,attempts=M_attempts) for r in range(0,repeats)]
for fraction in fractions_unknown
]
''' Make sure each M has no empty rows or columns '''
def check_empty_rows_columns(M,fraction):
sums_columns = M.sum(axis=0)
sums_rows = M.sum(axis=1)
for i,c in enumerate(sums_rows):
assert c != 0, "Fully unobserved row in M, row %s. Fraction %s." % (i,fraction)
for j,c in enumerate(sums_columns):
assert c != 0, "Fully unobserved column in M, column %s. Fraction %s." % (j,fraction)
for Ms_train_test,fraction in zip(all_Ms_train_test,fractions_unknown):
for (M_train,M_test) in Ms_train_test:
check_empty_rows_columns(M_train,fraction)
R_ctrp, M_ctrp, cell_lines, drugs = load_data_without_empty(
location_data + "ctrp_ec50_row_01.txt")
R_ccle_ec, M_ccle_ec = load_data_filter(location_data + "ccle_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)
#''' Seed all of the methods the same '''
#numpy.random.seed(0)
#random.seed(0)
''' Generate matrices M - one list of (M_train,M_test)'s for each fraction '''
M_attempts = 10000
all_Ms_train_test = [[
try_generate_M_from_M(M=M_ctrp, fraction=fraction, attempts=M_attempts)
for r in range(0, repeats)
] for fraction in fractions_unknown]
''' Make sure each M has no empty rows or columns '''
def check_empty_rows_columns(M, fraction):
sums_columns = M.sum(axis=0)
sums_rows = M.sum(axis=1)
for i, c in enumerate(sums_rows):
assert c != 0, "Fully unobserved row in M, row %s. Fraction %s." % (
i, fraction)
for j, c in enumerate(sums_columns):
assert c != 0, "Fully unobserved column in M, column %s. Fraction %s." % (
j, fraction)
priors = {'alpha': alpha, 'beta': beta, 'lambdaU': lambdaU, 'lambdaV': lambdaV}
metrics = ['MSE', 'R^2', 'Rp']
''' Load data '''
location = project_location + "DI_MMTF/data/datasets_drug_sensitivity/overlap/"
location_data = location + "data_row_01/"
R, M_original, _, _ = load_data_without_empty(location_data +
"ctrp_ec50_row_01.txt")
#''' Seed all of the methods the same '''
#numpy.random.seed(0)
#random.seed(0)
''' Generate matrices M - one list of (M_train,M_test)'s for each fraction '''
M_attempts = 10000
all_Ms_train_test = [[
try_generate_M_from_M(M=M_original, fraction=fraction, attempts=M_attempts)
for r in range(0, repeats)
] for fraction in fractions_unknown]
''' Make sure each M has no empty rows or columns '''
def check_empty_rows_columns(M, fraction):
sums_columns = M.sum(axis=0)
sums_rows = M.sum(axis=1)
for i, c in enumerate(sums_rows):
assert c != 0, "Fully unobserved row in M, row %s. Fraction %s." % (
i, fraction)
for j, c in enumerate(sums_columns):
assert c != 0, "Fully unobserved column in M, column %s. Fraction %s." % (
j, fraction)
