|
|
|
@ -2,19 +2,27 @@ |
|
|
|
|
|
|
|
|
|
import glob |
|
|
|
|
import os |
|
|
|
|
from pathlib import Path |
|
|
|
|
from memory_profiler import profile |
|
|
|
|
|
|
|
|
|
from pyctbn.legacy import JsonImporter |
|
|
|
|
from pyctbn.legacy import SamplePath |
|
|
|
|
from pyctbn.legacy import StructureConstraintBasedEstimator |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@profile |
|
|
|
|
def structure_constraint_based_estimation_example(): |
|
|
|
|
Path("./data").mkdir(parents=True, exist_ok=True) |
|
|
|
|
# <read the json files in ./data path> |
|
|
|
|
read_files = glob.glob(os.path.join('./', "*.json")) |
|
|
|
|
read_files = glob.glob(os.path.join("./data/", "*.json")) |
|
|
|
|
# <initialize a JsonImporter object for the first file> |
|
|
|
|
importer = JsonImporter(file_path=read_files[0], samples_label='samples', |
|
|
|
|
structure_label='dyn.str', variables_label='variables', |
|
|
|
|
time_key='Time', variables_key='Name') |
|
|
|
|
importer = JsonImporter( |
|
|
|
|
file_path=read_files[0], |
|
|
|
|
samples_label='samples', |
|
|
|
|
structure_label='dyn.str', |
|
|
|
|
variables_label='variables', |
|
|
|
|
time_key='Time', |
|
|
|
|
variables_key='Name' |
|
|
|
|
) |
|
|
|
|
# <import the data at index 0 of the outer json array> |
|
|
|
|
importer.import_data(0) |
|
|
|
|
# construct a SamplePath Object passing a filled AbstractImporter object |
|
|
|
@ -27,17 +35,23 @@ def structure_constraint_based_estimation_example(): |
|
|
|
|
# and the independence tests significance, if you have prior knowledge about |
|
|
|
|
# the net structure create a list of tuples |
|
|
|
|
# that contains them and pass it as known_edges parameter |
|
|
|
|
se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1, |
|
|
|
|
known_edges=[], thumb_threshold=25) |
|
|
|
|
se1 = StructureConstraintBasedEstimator( |
|
|
|
|
sample_path=s1, |
|
|
|
|
exp_test_alfa=0.1, |
|
|
|
|
chi_test_alfa=0.1, |
|
|
|
|
known_edges=[], |
|
|
|
|
thumb_threshold=25 |
|
|
|
|
) |
|
|
|
|
# call the algorithm to estimate the structure |
|
|
|
|
se1.estimate_structure() |
|
|
|
|
# obtain the adjacency matrix of the estimated structure |
|
|
|
|
print(se1.adjacency_matrix()) |
|
|
|
|
Path("./res").mkdir(parents=True, exist_ok=True) |
|
|
|
|
# save the estimated structure to a json file |
|
|
|
|
# (remember to specify the path AND the .json extension).... |
|
|
|
|
se1.save_results('./results0.json') |
|
|
|
|
se1.save_results("./res/results0.json") |
|
|
|
|
# ...or save it also in a graphical model fashion |
|
|
|
|
# (remember to specify the path AND the .png extension) |
|
|
|
|
se1.save_plot_estimated_structure_graph('./result0.png') |
|
|
|
|
se1.save_plot_estimated_structure_graph("./res/result0.png") |
|
|
|
|
|
|
|
|
|
structure_constraint_based_estimation_example() |
|
|
|
|