unimib-thesis-mad
/
PyCTBN
Archived
1
0
Fork 0
Code Releases Activity

Updated examples file

Browse Source
master
Luca Moretti 4 years ago
parent e173978bf4
commit 02dc6abd27
1 changed files with 178 additions and 0 deletions
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Show Stats Download Patch File Download Diff File
  1. 178
      README.rst

178
README.rst
Unescape View File

@ -84,7 +84,14 @@ Implementing your own data importer
#...and the Structure object with all the process data
s1.build_structure()
Structure Estimation Examples
##############################
| In this section some examples will be shown in order to provide some useful information about the usage of the library
Constraint based estimation
****************************
| This example shows how to estimate the structure given a series of trajectories using a constraint based approach.
| The first three instructions import all the necessary data (trajectories, nodes cardinalities, nodes labels),
@ -147,3 +154,174 @@ Structure Estimation Examples
# ...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')
Score based estimation with Hill Climbing
*****************************************
| This example shows how to estimate the structure given a series of trajectories using a score based approach
| and the Hill Climbing algorithm as optimization strategy.
| The structure of the code is the same as the previus example, but an explanation of the Structure score based estimator
| will be provided.
| Then an estimator object is created, in this case a score based estimator,
| it necessary to pass a SamplePath object where build_trajectories and build_structure methods have already been called.
| If you have prior knowledge about the net structure pass it to the constructor with the known_edges parameter.
| The other parameters are contextual to the StructureScoreBasedEstimator, see the documentation for more details.
| To estimate the structure simply call the estimate_structure method passing the desidered parameters, such as the
| optimization strategy, or simply use the default configuration.
| In this case an Hill Climbing approch is choosen.
.. code-block:: python
import glob
import os
from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import StructureScoreBasedEstimator
def structure_constraint_based_estimation_example():
# <read the json files in ./data path>
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')
# <import the data at index 0 of the outer json array>
importer.import_data(0)
# construct a SamplePath Object passing a filled AbstractImporter object
s1 = SamplePath(importer=importer)
# build the trajectories
s1.build_trajectories()
# build the information about the net
s1.build_structure()
# construct a StructureEstimator object passing a correctly build SamplePath object
# and hyperparameters tau and alpha, 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 = StructureScoreBasedEstimator(sample_path=s1, tau_xu = 0.1, alpha_xu = 1,
known_edges=[])
# call the algorithm to estimate the structure
# and pass all the desidered parameters, in this case an Hill Climbing approach
# will be selected as optimization strategy.
se1.estimate_structure(
max_parents = None,
iterations_number = 40,
patience = None,
optimizer = 'hill'
)
# obtain the adjacency matrix of the estimated structure
print(se1.adjacency_matrix())
# save the estimated structure to a json file
# (remember to specify the path AND the .json extension)....
se1.save_results('./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')
Score based estimation with Tabu Search and Data Augmentation
**************************************************************
| This example shows how to estimate the structure given a series of trajectories using a score based approach
| and the Tabu Search algorithm as optimization strategy and how to use a data augmentation strategy to increase the
| number of data available.
| The structure of the code is the same as the previus example, but an explanation of the data augmentation technique
| will be provided.
| In this case a SampleImporter is used to import the data instead of a JsonImporter.
| Using a SampleImporter requires the user to read the data and put it into different lists or DataFrames before to
| inizialize the SampleImporter instance.
| Then it is possible to increase the amount of data by using one of the external libraries who provide data augmentation
| approaches, in this example sklearn is used.
| Then all the information can be passed to the SampleImporter constructor and the import_data method can be used to provide
| the preprossing operations of the PyCTBN library.
| Then an estimator object is created, in this case a score based estimator,
| it necessary to pass a SamplePath object where build_trajectories and build_structure methods have already been called.
| If you have prior knowledge about the net structure pass it to the constructor with the known_edges parameter.
| The other parameters are contextual to the StructureScoreBasedEstimator, see the documentation for more details.
| To estimate the structure simply call the estimate_structure method passing the desidered parameters, such as the
| optimization strategy, or simply use the default configuration.
| In this case an Hill Climbing approch is choosen.
.. code-block:: python
import glob
import os
from sklearn.utils import resample
from PyCTBN import SampleImporter
from PyCTBN import SamplePath
from PyCTBN import StructureScoreBasedEstimator
def structure_constraint_based_estimation_example():
# <read the json files in ./data path>
read_files = glob.glob(os.path.join('./data', "*.json"))
# read the first file in the directory (or pass the file path)
with open(file_path=read_files[0]) as f:
raw_data = json.load(f)
# read the variables information
variables= pd.DataFrame(raw_data[0]["variables"])
# read the prior information if they are given
prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"])
#read the samples
trajectory_list_raw= raw_data[0]["samples"]
#convert them in DataFrame
trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw]
# use an external library in order to provide the data augmentation operations, in this case
# sklearn.utils is used
augmented_trajectory_list = resample (trajectory_list, replace = True, n_samples = 300 )
# <initialize a SampleImporter object using the data read before>
importer = SampleImporter(
trajectory_list = augmented_trajectory_list,
variables=variables,
prior_net_structure=prior_net_structure
)
# <import the data>
importer.import_data()
# construct a SamplePath Object passing a filled AbstractImporter object
s1 = SamplePath(importer=importer)
# build the trajectories
s1.build_trajectories()
# build the information about the net
s1.build_structure()
# construct a StructureEstimator object passing a correctly build SamplePath object
# and hyperparameters tau and alpha, 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 = StructureScoreBasedEstimator(sample_path=s1, tau_xu = 0.1, alpha_xu = 1,
known_edges=[])
# call the algorithm to estimate the structure
# and pass all the desidered parameters, in this case a Tabu Search approach
# will be selected as optimization strategy. It is possible to select the tabu list length and
# the tabu rules duration, and the other parameters as in the previus example.
se1.estimate_structure(
max_parents = None,
iterations_number = 100,
patience = 20,
optimizer = 'tabu',
tabu_length = 10,
tabu_rules_duration = 10
)
# obtain the adjacency matrix of the estimated structure
print(se1.adjacency_matrix())
# save the estimated structure to a json file
# (remember to specify the path AND the .json extension)....
se1.save_results('./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')