def testVvGlobCoup(data, true_inc):
output_line = (
'Varying Variances Globally Coupled Bayesian Piece-Wise Linear Regression with moves on '
+ 'change-points and parent sets on Yeast Data.')
print(output_line)
logger.info(output_line) # Print and write output
baNet = Network(data, args.chain_length, args.burn_in)
baNet.infer_network('var_glob_coup_nh_dbn')
adjMatrixRoc(baNet.proposed_adj_matrix, true_inc, args.verbose)
# save the chain into the output folder
save_chain('vv_glob_coup_dbn.pckl', baNet)
def testSeqCoupPwBlrWithCpsParentMoves(data, true_inc):
output_line = (
'Sequentially Coupled Bayesian Piece-Wise Linear Regression with moves on '
+ 'change-points and parent sets on Yeast data.')
print(output_line)
logger.info(output_line) # Print and write output
baNet = Network(data, args.chain_length, args.burn_in)
baNet.infer_network('seq_coup_nh_dbn')
adjMatrixRoc(baNet.proposed_adj_matrix, true_inc, args.verbose)
# save the chain into the output folder
save_chain('seq_coup_dbn.pckl', baNet)
def testPwBlrWithCpsParentMoves(data, true_inc):
output_line = ('Bayesian Piece-Wise Linear Regression with moves on ' +
'change-points and parent sets for the Yeast data.')
print(output_line)
logger.info(output_line) # Print and write output
if args.change_points == 0:
args.change_points = []
args.change_points.append(data.shape[0] +
1) # append the len data + 1 so the algo works
baNet = Network(data, args.chain_length, args.burn_in)
baNet.infer_network('varying_nh_dbn')
adjMatrixRoc(baNet.proposed_adj_matrix, true_inc, args.verbose)
# save the chain into the output folder
save_chain('nh_dbn.pckl', baNet)
def testPwBlrWithParentMoves(data, true_inc):
output_line = (
'Bayesian Piece-Wise Linear Regression with moves on ' +
'the parent set only with fixed changepoints for the Yeast Data. \n')
print(output_line)
logger.info(output_line) # Print and write output
if args.change_points == 0:
args.change_points = []
args.change_points.append(data.shape[0] +
1) # append the len data + 1 so the algo works
baNet = Network(data, args.chain_length, args.burn_in,
args.change_points) # Create theh BN obj
baNet.infer_network(
'fixed_nh_dbn') # Do the fixed changepoints version of the DBN algo
adjMatrixRoc(baNet.proposed_adj_matrix, true_inc,
args.verbose) # check the ROC
def testPwBlrWithCpsParentMoves(coefs):
output_line = ('Bayesian Piece-Wise Linear Regression with moves on' +
'change-points and parent sets.')
print(output_line)
logger.info(output_line) # Print and write output
# Generate data to test our algo
network, _, adjMatrix = generateNetwork(args.num_features, args.num_indep,
coefs, args.num_samples,
args.change_points, args.verbose,
args.generated_noise_var)
baNet = Network(network, args.chain_length, args.burn_in)
baNet.infer_network('varying_nh_dbn')
trueAdjMatrix = adjMatrix[
0] # For the moment we just get the adj matrix of the first cp
adjMatrixRoc(baNet.proposed_adj_matrix, trueAdjMatrix, args.verbose)
def testPwBlrWithParentMoves(coefs):
output_line = ('Bayesian Piece-Wise Linear Regression with moves on' +
'the parent set only with fixed changepoints. \n')
print(output_line)
logger.info(output_line) # Print and write output
# Generate data to test our algo
network, _, adjMatrix = generateNetwork(args.num_features, args.num_indep,
coefs, args.num_samples,
args.change_points, args.verbose,
args.generated_noise_var)
baNet = Network(network, args.chain_length, args.burn_in,
args.change_points) # Create theh BN obj
baNet.infer_network(
'fixed_nh_dbn') # Do the fixed chnagepoints version of the DBN algo
trueAdjMatrix = adjMatrix[
0] # For the moment we just get the adj matrix of the first cp
adjMatrixRoc(baNet.proposed_adj_matrix, trueAdjMatrix, args.verbose)
def test_h_dbn(data, true_inc):
output_line = ('Bayesian Linear Regression with moves on ' +
'the parent set only for the Yeast data. \n')
print(output_line)
logger.info(output_line) # Print and write output
change_points = [
] # set the cps empty list because this is the homegeneous version
# Create/Call the Network objects/methods
baNet = Network(data, args.chain_length, args.burn_in,
args.change_points) # Create theh BN obj
baNet.infer_network(
'h_dbn') # Do the fixed parents version of the DBN algo
# trueAdjMatrix = adjMatrix[0] # For the moment we just get the adj matrix of the first cp
adjMatrixRoc(baNet.proposed_adj_matrix, true_inc, args.verbose)
# save the chain into the output folder
save_chain('h_dbn.pckl', baNet)
def test_h_dbn(coefs):
output_line = ('Bayesian Linear Regression with moves on' +
'the parent set only. \n')
print(output_line)
logger.info(output_line) # Print and write output
change_points = [
] # set the cps empty list because this is the homegeneous version
# Generate data to test our algo
network, _, adjMatrix = generateNetwork(args.num_features, args.num_indep,
coefs, args.num_samples,
change_points, args.verbose,
args.generated_noise_var)
baNet = Network(network, args.chain_length, args.burn_in,
args.change_points) # Create theh BN obj
baNet.infer_network(
'h_dbn') # Do the fixed parents version of the DBN algo
trueAdjMatrix = adjMatrix[
0] # For the moment we just get the adj matrix of the first cp
adjMatrixRoc(baNet.proposed_adj_matrix, trueAdjMatrix, args.verbose)
