class CombineCosineRescalEvaluation(EvaluationAlgorithm):
def __init__(self, args, output_folder, logger, ground_truth, start_time):
self.init(args, output_folder, logger, ground_truth, start_time)
self.report2 = EvaluationReport(logger, args.fbeta)
def log1(self):
self.logger.info('First step for prediction of cosine similarity with threshold: %f:' %
float(self.args.cosine_rescal[2]))
def log2(self):
self.logger.info('And second step for combined RESCAL prediction with parameters: %d, %f:'
% (int(self.args.cosine_rescal[0]), float(self.args.cosine_rescal[1])))
def evaluate_fold(self, test_tensor, test_needs, idx_test):
cosine_pred, rescal_pred = self.predict_combine_cosine_rescal(
test_tensor, test_needs, idx_test, int(self.args.cosine_rescal[0]),
float(self.args.cosine_rescal[1]), float(self.args.cosine_rescal[2]),
bool(self.args.cosine_rescal[3]))
self.log1()
self.report.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE, idx_test), cosine_pred)
self.log2()
self.report2.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE,idx_test), rescal_pred)
def finish_evaluation(self):
self.log1()
self.report.summary()
self.log2()
self.report2.summary()
# predict connections by combining the execution of algorithms
def predict_combine_cosine_rescal(self, input_tensor, test_needs, idx_test, rank,
rescal_threshold, cosine_threshold, useNeedTypeSlice):
wants = input_tensor.getWantIndices()
offers = input_tensor.getOfferIndices()
# execute the cosine algorithm first
binary_pred_cosine = cosinus_link_prediciton(input_tensor, test_needs, cosine_threshold, 0.0, False)
# use the connection prediction of the cosine algorithm as input for rescal
temp_tensor = input_tensor.copy()
temp_tensor.addSliceMatrix(binary_pred_cosine, SparseTensor.CONNECTION_SLICE)
A,R = execute_rescal(temp_tensor, rank)
P_bin = predict_rescal_connections_by_threshold(A, R, rescal_threshold, offers, wants, test_needs)
# return both predictions the earlier cosine and the combined rescal
binary_pred_cosine = binary_pred_cosine[idx_test]
binary_pred_rescal = matrix_to_array(P_bin, idx_test)
return binary_pred_cosine, binary_pred_rescal
class IntersectionCosineRescalEvaluation(EvaluationAlgorithm):
def __init__(self, args, output_folder, logger, ground_truth, start_time):
self.init(args, output_folder, logger, ground_truth, start_time)
self.report2 = EvaluationReport(logger, args.fbeta)
self.report3 = EvaluationReport(logger, args.fbeta)
def log1(self):
self.logger.info('Intersection of predictions of cosine similarity and rescal algorithms: ')
def log2(self):
self.logger.info('For RESCAL prediction with threshold %f:' % float(self.args.intersection[1]))
def log3(self):
self.logger.info('For prediction of cosine similarity between needs with thresholds: %f:' %
float(self.args.intersection[2]))
def evaluate_fold(self, test_tensor, test_needs, idx_test):
inter_pred, cosine_pred, rescal_pred = self.predict_intersect_cosine_rescal(
test_tensor, test_needs, idx_test, int(self.args.intersection[0]),
float(self.args.intersection[1]), float(self.args.intersection[2]),
bool(self.args.intersection[3]))
self.log1()
self.report.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE, idx_test), inter_pred)
self.log2()
self.report2.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE,idx_test), rescal_pred)
self.log3()
self.report3.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE,idx_test), cosine_pred)
def finish_evaluation(self):
self.log1()
self.report.summary()
self.log2()
self.report2.summary()
self.log3()
self.report3.summary()
# predict connections by intersection of RESCAL and cosine results
def predict_intersect_cosine_rescal(self, input_tensor, test_needs, idx_test, rank,
rescal_threshold, cosine_threshold, useNeedTypeSlice):
wants = input_tensor.getWantIndices()
offers = input_tensor.getOfferIndices()
# execute the cosine algorithm
binary_pred_cosine = cosinus_link_prediciton(input_tensor, test_needs, cosine_threshold, 0.0, False)
# execute the rescal algorithm
A,R = execute_rescal(input_tensor, rank)
P_bin = predict_rescal_connections_by_threshold(A, R, rescal_threshold, offers, wants, test_needs)
# return the intersection of the prediction of both algorithms
binary_pred_cosine = matrix_to_array(binary_pred_cosine, idx_test)
binary_pred_rescal = matrix_to_array(P_bin, idx_test)
binary_pred = [min(binary_pred_cosine[i], binary_pred_rescal[i]) for i in range(len(binary_pred_cosine))]
return binary_pred, binary_pred_cosine, binary_pred_rescal
def __init__(self, args, output_folder, logger, ground_truth, start_time):
self.init(args, output_folder, logger, ground_truth, start_time)
self.report2 = EvaluationReport(logger, args.fbeta)
self.report3 = EvaluationReport(logger, args.fbeta)
