def get_example_data():
# read the tensor from the folder passed by args
data_file_prefix = sys.argv[1]
header_file = data_file_prefix + '/headers.txt'
data_files = [data_file_prefix + "/connection.mtx",
data_file_prefix + "/needtype.mtx",
data_file_prefix + "/subject.mtx",
data_file_prefix + "/content.mtx",
data_file_prefix + "/category.mtx"]
slices = [SparseTensor.CONNECTION_SLICE, SparseTensor.NEED_TYPE_SLICE, SparseTensor.ATTR_SUBJECT_SLICE,
SparseTensor.ATTR_CONTENT_SLICE, SparseTensor.CATEGORY_SLICE]
tensor = read_input_tensor(header_file, data_files, slices, False)
data = []
target = []
# Store the chosen input into lists.
for need_index in tensor.getNeedIndices():
content = ""
category_index = tensor.getSliceMatrix(SparseTensor.CATEGORY_SLICE)[need_index,].nonzero()[1].tolist()
target.append(category_index)
for word in tensor.getAttributesForNeed(need_index, SparseTensor.ATTR_SUBJECT_SLICE):
content += word + " "
data.append(content)
# Print out the input, just a check:
target_names = tensor.getHeaders()
print("test")
print data
print target_names
print target
return data, target, target_names
def get_example_data():
# read the tensor from the folder passed by args
data_file_prefix = sys.argv[1]
header_file = data_file_prefix + '/headers.txt'
data_files = [data_file_prefix + "/connection.mtx",
data_file_prefix + "/needtype.mtx",
data_file_prefix + "/subject.mtx",
data_file_prefix + "/content.mtx",
data_file_prefix + "/category.mtx"]
slices = [SparseTensor.CONNECTION_SLICE, SparseTensor.NEED_TYPE_SLICE, SparseTensor.ATTR_SUBJECT_SLICE,
SparseTensor.ATTR_CONTENT_SLICE, SparseTensor.CATEGORY_SLICE]
tensor = read_input_tensor(header_file, data_files, slices, False)
data = []
target = []
# Store the chosen input into lists.
# The "if" statement is meant to include only samples with a single category (No multilabel)
for need_index in tensor.getNeedIndices():
content = ""
categories = tensor.getAttributesForNeed(need_index, SparseTensor.CATEGORY_SLICE)
numCategories = len(categories)
if numCategories >= 1:
category_index = tensor.getSliceMatrix(SparseTensor.CATEGORY_SLICE)[need_index,].nonzero()[1][0]
target.append(category_index)
for word in tensor.getAttributesForNeed(need_index, SparseTensor.ATTR_SUBJECT_SLICE):
content += word + " "
data.append(content)
# Include only few of all the categories (e.g. with samples > n)
newdata = []
newtarget = []
for i in range(len(target)):
if target.count(target[i]) > 50:
newtarget.append(target[i])
newdata.append(data[i])
data = newdata
target = newtarget
# Print out the input, just a check:
target_names = tensor.getHeaders()
print("test")
print data
print target_names
print target
return data, target, target_names
help="threshold of rescal algorithm to produce hints")
args = parser.parse_args()
# load the tensor
header_file = "headers.txt"
slice_files = []
for file in os.listdir(args.inputfolder):
if file.endswith(".mtx"):
slice_files.append(file)
header_input = args.inputfolder + "/" + header_file
data_input = []
for slice in slice_files:
data_input.append(args.inputfolder + "/" + slice)
input_tensor = read_input_tensor(header_input, data_input, True)
# execute rescal
A, R = execute_extrescal(input_tensor, args.rank)
# predict new hints
_log.info("predict hints with threshold: %f" % args.threshold)
mask_matrix = create_hint_mask_matrix(input_tensor)
connection_prediction = predict_rescal_hints_by_threshold(
A, R, args.threshold, mask_matrix)
_log.info("number of hints created: %d" %
len(connection_prediction.nonzero()[0]))
# write the hint output matrix
output = args.outputfolder + "/" + "hints.mtx"
_log.info("write hint prediction output matrix: " + output)
# load the tensor
header_file = "headers.txt"
atom_indices_file = "atomIndices.txt"
slice_files = []
for file in os.listdir(args.inputfolder):
if file.endswith(".mtx"):
slice_files.append(file)
header_input = args.inputfolder + "/" + header_file
atom_indices_input = args.inputfolder + "/" + atom_indices_file
data_input = []
for slice in slice_files:
data_input.append(args.inputfolder + "/" + slice)
input_tensor = read_input_tensor(header_input, atom_indices_input,
data_input, True)
# execute rescal
A, R = execute_extrescal(input_tensor, args.rank)
# predict new hints
_log.info("predict hints with threshold: %f" % args.threshold)
connection_prediction = predict_rescal_hints_by_threshold(
A, R, args.threshold, input_tensor)
_log.info("number of hints created: %d" %
len(connection_prediction.nonzero()[0]))
# write the hint output matrix
output = args.outputfolder + "/" + "hints.mtx"
_log.info("write hint prediction output matrix: " + output)
def __init__(self, args, output_folder, logger, ground_truth, start_time):
self.init(args, output_folder, logger, ground_truth, start_time)
header_input = args.inputfolder + "/" + args.headers
self.file_prediction_tensor = read_input_tensor(
header_input, [args.prediction_matrix_file], [SparseTensor.CONNECTION_SLICE], True)
if args.outputfolder:
outfolder = args.outputfolder
else:
outfolder = folder + "/out/" + start_time
if not os.path.exists(outfolder):
os.makedirs(outfolder)
hdlr = logging.FileHandler(outfolder + "/eval_result_" + start_time + ".log")
_log.addHandler(hdlr)
# load the tensor input data
data_input = [folder + "/" + args.connection_slice, folder + "/" + args.needtype_slice]
for slice in args.additional_slices:
data_input.append(folder + "/" + slice)
header_input = folder + "/" + args.headers
slices = SparseTensor.defaultSlices + [SparseTensor.ATTR_CONTENT_SLICE, SparseTensor.CATEGORY_SLICE]
input_tensor = read_input_tensor(header_input, data_input, slices, True)
# TEST-PARAMETERS:
# ===================
# (10-)fold cross validation
FOLDS = args.folds
# True means: for testing mask all connections of random test needs (Test Case: Predict connections for new need
# without connections)
# False means: for testing mask random connections (Test Case: Predict connections for existing need which may
# already have connections)
MASK_ALL_CONNECTIONS_OF_TEST_NEED = not args.maskrandom
# by changing this parameter the number of training connections per need can be set. Choose a high value (e.g.
# 100) to use all connection in the connections file. Choose a low number to restrict the number of training
