def pcc_item_rating_pred(path, rating, method, k):
start = time.time()
name = 'pcc_item'
data = extract_data(path)
mtx = get_matrix(3).toarray()
item_mtx = []
result = []
zero = np.where(~mtx.any(axis=0))[0] # get zero
mtx[:, [zero]] = 0.00001 # prevent zero-devide
#normalize
pcc = (mtx.T - np.sum(mtx, axis=1)) / len(mtx)
pcc /= np.linalg.norm(mtx, axis=1).T
mtx = pcc.T
if method == 'dot':
item_mtx = dot_sim(mtx, name)
elif method == 'cos':
inputs = (mtx.T * np.linalg.norm(mtx, axis=1)).T
item_mtx = cos_sim(inputs, name)
#KNN
for i in data:
score = 0
item_id = i[0] #get item_id
user_id = i[1] #get user_id
item = item_mtx[item_id] #row
knn = np.argsort(item, kind='heapsort')[::-1][0:k + 1]
if item_id in knn: # delte query
idx = np.where(knn == item_id)
knn = np.delete(knn, idx)
else:
knn = np.delete(knn, len(knn) - 1)
#get score
if rating == 'mean':
score = np.sum(np.take(mtx[:, user_id],
knn.tolist())) / float(k) + 3
elif rating == 'weighted':
knn_sim = item[knn]
if np.sum(knn_sim) != 0: #prevent zero-devide
weight = knn_sim / np.sum(knn_sim)
score = np.sum(
np.multiply(np.take(mtx[:, user_id], knn.tolist()),
weight)) + 3
else:
score = np.sum(mtx[:, user_id]) / np.size(
np.nonzero(mtx[:, user_id])) + 3
result.append(score)
write(result, name, rating, method, k)
print('item_rating_pred {} {} {} time : {}'.format(method, rating, k,
time.time() - start))
gold = golden()
print("RMSE :", np.sqrt(np.mean(np.square(result - gold))))
def user_rating_pred(path, rating,method,k):
start = time.time()
name='user'
data = extract_data(path)
mtx = get_matrix(3).toarray()
user_mtx = []
result = []
zero = np.where(~mtx.any(axis=0))[0] #get zero
mtx[:, [zero]] = 0.00001 # prevent zero-devide
if method =='dot':
user_mtx = dot_sim(mtx,name)
elif method=='cos':
inputs=np.linalg.norm(mtx,axis=0)*mtx #normalize before cos_sim
user_mtx = cos_sim(inputs,name)#honestly cos_sim is cosine similariy but input is normalized so same with cos_similarity
for i in data:
score = 0
mv_id = i[0] #get item_id
user_id = i[1] #get user_id
user = user_mtx[user_id] #get user
knn = np.argsort(user,kind='heapsort')[::-1][0: k+1]
if user_id in knn:# delte query
i = np.where(knn == user_id)
knn = np.delete(knn, i)
else:
knn = np.delete(knn, len(knn) - 1)
#get score
if rating == 'mean':
score = (np.sum(np.take(mtx[mv_id, :], knn.tolist())) / float(k))+3
elif rating=='weighted':
knn_sim = user[knn]
if np.sum(knn_sim) != 0:
weight = knn_sim / np.sum(knn_sim) #prevent zero-devide
score = np.sum(np.multiply(np.take(mtx[mv_id, :], knn.tolist()), weight))+3
else:
score = np.sum(mtx[mv_id, :]) / np.size(np.nonzero(mtx[mv_id, :]))+3
result.append(score)
#print('start _writting')
write(result,name,rating,method,k)
print('user_rating_pred {} {} {} time : {}'.format(method,rating, k,time.time() - start))
gold=golden()
print("RMSE :",np.sqrt(np.mean(np.square(result-gold))))
def main():
from optparse import OptionParser
usage = "usage: %prog [options] -i [INPUT_MAF_FILE_FOLDER] -o [OUTPUT_FOLDER] [OPTIONAL_FLAGS]"
parser = OptionParser(usage=usage)
# required flags
parser.add_option(
"-i",
"--i",
dest="input",
nargs=2,
default=None,
help=
"Enter mutation maf files path,if cancer vs normal the second arg is None."
)
parser.add_option("-o",
"--out",
dest="out",
nargs=1,
default=None,
help="Enter an output folder.")
# optional flags
parser.add_option("-f",
"--fdr",
dest="fdr",
nargs=1,
default=0.05,
help="FDR cut off")
parser.add_option(
"-s",
"--step",
dest="step",
nargs=1,
default=1,
help=
"The maximum number of interval genes allowed between Sub-pathway genes."
)
parser.add_option("-c",
"--minsize",
dest="minsize",
nargs=1,
default=3,
help="Sub-pathway minimum number of nodes.")
parser.add_option(
"-p",
"--pathway",
dest="pathway",
nargs=1,
default=None,
help="KEGG human normal pathway filename(absolute path).")
parser.add_option(
"-g",
"--gene",
dest="gene",
nargs=1,
default=None,
help=
"gene information file, gene id and gene symbol,default is NCBI human_gene_info."
)
parser.add_option("-n",
"--nperm",
dest="nperm",
nargs=1,
default=1000,
help="random times")
parser.add_option(
"-m",
"--symbol",
dest="symbol",
nargs=1,
default=0,
help="input 1:mutation maf file geneid is 0 but have symbol.")
parser.add_option(
"-a",
"--sub",
dest="sub",
nargs=1,
default=None,
help=
"Determine whether subpath extraction is performed separately.File absolute path."
)
# RETRIEVING FLAGS
(options, args) = parser.parse_args()
if not options.input or not options.out:
print('hi there')
parser.print_help()
exit()
# making the out folder if it doesn't exist
outfolder = myutils.folderparser(options.out)
#get pathway information
if not options.pathway:
pathway_info = myselect.select_normal_pathway_gene()
else:
pathway_info = myutils.getpathway(options.pathway)
# get sample mutation infromation
mut = []
for t in options.input:
if t != 'None':
mut.append(myselect.select_mutation_gene(t, int(options.symbol)))
# get gene information
if not options.gene:
gene_info = myutils.get_gene_info()
else:
gene_info = myutils.get_gene_info(options.gene)
# run
gid = myutils.getgid()
hsa = myselect.select_human_pathway().set_index('pathway name')
begin = """
**********************************************************************
* BEGIN *
**********************************************************************
"""
end = """
**********************************************************************
* END *
**********************************************************************
"""
if len(mut) == 1:
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 高覆盖通路 #
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
if not options.sub:
tb = time.time()
print('-' * 10, 'Non-Random Mutation-High-Cover Pathway', '-' * 10)
print(begin)
sig_pathway = myalgorithm.RSMP(mut[0], pathway_info, gid,
int(options.nperm),
float(options.fdr))
myutils.write(sig_pathway,
os.path.join(outfolder, 'sig_pathway.xlsx'))
te = time.time()
print('Spend %.2f minute!' % ((te - tb) / 60))
print(end)
else:
sig_pathway = pd.read_excel(options.sub)
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# common sub-pathways #
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
te = time.time()
print('-' * 10, 'Mutation-High-Cover Sub-Pathway', '-' * 10)
print(begin)
Sub_Pathway = SubPathway.HighCoverSub(mut[0], gene_info,
sig_pathway, hsa, outfolder,
int(options.step),
int(options.minsize))
myutils.write(Sub_Pathway, os.path.join(outfolder, 'Sub_Pathway.xlsx'))
print('Spend %.2f minute' % ((time.time() - te) / 60))
print(end)
else:
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# subtype-specific pathways #
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
if not options.sub:
tb = time.time()
print('-' * 15, 'Subtype Specificity Pathway', '-' * 15)
print(begin)
sig_pathway = myalgorithm.TSDP(mut[0],
mut[1],
pathway_info,
cut_off=float(options.fdr))
myutils.write(sig_pathway,
os.path.join(outfolder, 'sig_pathway.xlsx'))
te = time.time()
print('Spend %.2f minute!' % ((te - tb) / 60))
print(end)
else:
sig_pathway = pd.read_excel(options.sub)
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# subtype-specific sub-pathways #
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
te = time.time()
print('-' * 15, 'Subtype Specificity Sub-Pathway', '-' * 15)
print(begin)
Specific_Sub_Pathway = SubPathway.DistinctSub(mut, gene_info,
sig_pathway, hsa,
outfolder,
int(options.step),
int(options.minsize))
myutils.write(Specific_Sub_Pathway,
os.path.join(outfolder, 'Specific_Sub_Pathway.xlsx'))
print('Spend %.2f minute' % ((time.time() - te) / 60))
print(end)