Doctorado-ML/bayesclass
1
0
Fork 0
mirror of https://github.com/Doctorado-ML/bayesclass.git synced 2025-08-16 16:15:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

Refator kdb with new BayesNetwork

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2023-07-08 10:40:33 +02:00
parent 260997c872
commit 36cc875615
1 changed files with 28 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
bayesclass/clfs.py
View File

@@ -16,6 +16,7 @@ from pgmpy.base import DAG
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from fimdlp.mdlp import FImdlp from fimdlp.mdlp import FImdlp
from .cppSelectFeatures import CSelectKBestWeighted from .cppSelectFeatures import CSelectKBestWeighted
from .BayesNet import BayesNetwork
from ._version import __version__ from ._version import __version__
@@ -166,17 +167,27 @@ class BayesBase(BaseEstimator, ClassifierMixin):
kwargs : dict kwargs : dict
fit parameters fit parameters
""" """
self.model_ = BayesianNetwork( # self.model_ = BayesianNetwork(
self.dag_.edges(), show_progress=self.show_progress # self.dag_.edges(), show_progress=self.show_progress
) # )
states = dict(state_names=kwargs.pop("state_names", [])) # states = dict(state_names=kwargs.pop("state_names", []))
self.model_.fit( # self.model_.fit(
self.dataset_, # self.dataset_,
estimator=BayesianEstimator, # estimator=BayesianEstimator,
prior_type="K2", # prior_type="K2",
weighted=self.weighted_, # weighted=self.weighted_,
**states, # **states,
) # )
self.model_ = BayesNetwork()
features = kwargs["features"]
for i, feature in enumerate(features):
maxf = max(self.X_[:, i] + 1)
self.model_.addNode(feature, maxf)
class_name = kwargs["class_name"]
self.model_.addNode(class_name, max(self.y_) + 1)
for source, destination in self.dag_.edges():
self.model_.addEdge(source, destination)
self.model_.fit(self.X_, self.y_, features, class_name)
def predict(self, X): def predict(self, X):
"""A reference implementation of a prediction for a classifier. """A reference implementation of a prediction for a classifier.
@@ -228,10 +239,11 @@ class BayesBase(BaseEstimator, ClassifierMixin):
check_is_fitted(self, ["X_", "y_", "fitted_"]) check_is_fitted(self, ["X_", "y_", "fitted_"])
# Input validation # Input validation
X = check_array(X) X = check_array(X)
dataset = pd.DataFrame( # dataset = pd.DataFrame(
X, columns=self.feature_names_in_, dtype=np.int32 # X, columns=self.feature_names_in_, dtype=np.int32
) # )
return self.model_.predict(dataset).values.ravel() # return self.model_.predict(dataset).values.ravel()
return self.model_.predict(X)
def plot(self, title="", node_size=800): def plot(self, title="", node_size=800):
warnings.simplefilter("ignore", UserWarning) warnings.simplefilter("ignore", UserWarning)
@@ -398,7 +410,7 @@ class KDB(BayesBase):
# 3. Let the used variable list, S, be empty. # 3. Let the used variable list, S, be empty.
S_nodes = [] S_nodes = []
# 4. Let the DAG being constructed, BN, begin with a single class node # 4. Let the DAG being constructed, BN, begin with a single class node
dag = BayesianNetwork(show_progress=self.show_progress) dag = BayesianNetwork()
dag.add_node(self.class_name_) # , state_names=self.classes_) dag.add_node(self.class_name_) # , state_names=self.classes_)
# 5. Repeat until S includes all domain features # 5. Repeat until S includes all domain features
# 5.1 Select feature Xmax which is not in S and has the largest value # 5.1 Select feature Xmax which is not in S and has the largest value