def evaluate_fold(self, test_tensor, test_needs, idx_test):
self.logEvaluationLine()
binary_pred = cosinus_link_prediciton(test_tensor, test_needs, self.threshold,
self.transitive_threshold, self.weighted)
self.report.add_evaluation_data(self.ground_truth.getArrayFromSliceMatrix(
SparseTensor.CONNECTION_SLICE, idx_test), matrix_to_array(binary_pred, idx_test))
if self.args.statistics:
self.evalDetails.add_statistic_details(
self.ground_truth.getSliceMatrix(SparseTensor.CONNECTION_SLICE),
binary_pred, idx_test)
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 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
