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https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-16 16:15:57 +00:00
Begin refactorization of new estimators
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@@ -462,73 +462,153 @@ class AODE(BayesBase, BaseEnsemble):
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return mode(result, axis=1, keepdims=False).mode.ravel()
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class TANNew(TAN):
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pass
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class KDBNew(KDB):
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def __init__(self, k, theta=0.3, show_progress=False, random_state=None):
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super().__init__(
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k, theta, show_progress=show_progress, random_state=random_state
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)
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self.estimator = Proposal(self)
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def fit(self, X, y, **kwargs):
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Xd, kwargs = self.estimator.discretize_train(X, y, kwargs)
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super().fit(Xd, y, **kwargs)
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upgraded, Xd, kwargs = self.estimator.local_discretization(kwargs)
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if upgraded:
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super().fit(Xd, y, **kwargs)
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def predict(self, X):
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return self.estimator.predict(X)
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# def fit(self, X, y, **kwargs):
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# self.discretizer_ = FImdlp(n_jobs=1)
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# Xd = self.discretizer_.fit_transform(X, y)
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# features = (
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# kwargs["features"]
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# if "features" in kwargs
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# else self.default_feature_names(Xd.shape[1])
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# )
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# self.compute_kwargs(Xd, y, kwargs)
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# # Build the model
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# super().fit(Xd, y, **kwargs)
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# self.idx_features_ = dict(list(zip(features, range(len(features)))))
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# self.proposal(Xd)
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# return self
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# def predict(self, X):
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# return super().predict(self.discretizer_.transform(X))
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# def compute_kwargs(self, Xd, y, kwargs):
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# features = kwargs["features"]
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# states = {
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# features[i]: np.unique(Xd[:, i]).tolist()
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# for i in range(Xd.shape[1])
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# }
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# class_name = (
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# kwargs["class_name"]
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# if "class_name" in kwargs
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# else self.default_class_name()
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# )
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# states[class_name] = np.unique(y).tolist()
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# kwargs["state_names"] = states
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# self.kwargs_ = kwargs
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# def check_integrity(self, X, state_names, features):
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# for i in range(X.shape[1]):
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# if not np.array_equal(
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# np.unique(X[:, i]), np.array(state_names[features[i]])
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# ):
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# print(
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# "i",
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# i,
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# "features[i]",
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# features[i],
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# "np.unique(X[:, i])",
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# np.unique(X[:, i]),
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# "np.array(state_names[features[i]])",
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# np.array(state_names[features[i]]),
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# )
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# raise ValueError("Discretization error")
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# def proposal(self, Xd):
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# """Discretize each feature with its fathers and the class"""
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# res = Xd.copy()
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# upgraded = False
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# for idx, feature in enumerate(self.feature_names_in_):
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# fathers = self.dag_.get_parents(feature)
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# if len(fathers) > 1:
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# # First remove the class name as it will be added later
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# fathers.remove(self.class_name_)
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# # Get the fathers indices
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# features = [self.idx_features_[f] for f in fathers]
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# # Update the discretization of the feature
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# res[:, idx] = self.discretizer_.join_fit(
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# target=idx, features=features, data=Xd
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# )
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# upgraded = True
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# if upgraded:
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# self.compute_kwargs(res, self.y_, self.kwargs_)
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# super().fit(res, self.y_, **self.kwargs_)
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class Proposal:
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def __init__(self, estimator):
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self.estimator = estimator
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def discretize_train(self, X, y, kwargs):
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self.discretizer_ = FImdlp(n_jobs=1)
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Xd = self.discretizer_.fit_transform(X, y)
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self.Xd = self.discretizer_.fit_transform(X, y)
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kwargs = self.compute_kwargs(y, kwargs)
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return self.Xd, kwargs
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def local_discretization(self, kwargs):
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features = kwargs["features"]
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self.idx_features_ = dict(list(zip(features, range(len(features)))))
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return self._local_discretization(kwargs)
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def predict(self, X):
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return self.estimator.predict(self.discretizer_.transform(X))
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def compute_kwargs(self, y, kwargs):
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features = (
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kwargs["features"]
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if "features" in kwargs
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else self.default_feature_names(Xd.shape[1])
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else self.estimator.default_feature_names(self.Xd.shape[1])
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)
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self.compute_kwargs(Xd, y, kwargs)
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# Build the model
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super().fit(Xd, y, **kwargs)
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self.idx_features_ = dict(list(zip(features, range(len(features)))))
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self.proposal(Xd)
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return self
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def predict(self, X):
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return super().predict(self.discretizer_.transform(X))
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def compute_kwargs(self, Xd, y, kwargs):
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features = kwargs["features"]
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states = {
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features[i]: np.unique(Xd[:, i]).tolist()
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for i in range(Xd.shape[1])
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features[i]: np.unique(self.Xd[:, i]).tolist()
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for i in range(self.Xd.shape[1])
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}
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class_name = (
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kwargs["class_name"]
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if "class_name" in kwargs
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else self.default_class_name()
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else self.estimator.default_class_name()
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)
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states[class_name] = np.unique(y).tolist()
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kwargs["state_names"] = states
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self.kwargs_ = kwargs
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kwargs["features"] = features
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kwargs["class_name"] = class_name
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return kwargs
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def check_integrity(self, X, state_names, features):
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for i in range(X.shape[1]):
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if not np.array_equal(
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np.unique(X[:, i]), np.array(state_names[features[i]])
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):
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print(
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"i",
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i,
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"features[i]",
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features[i],
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"np.unique(X[:, i])",
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np.unique(X[:, i]),
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"np.array(state_names[features[i]])",
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np.array(state_names[features[i]]),
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)
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raise ValueError("Discretization error")
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def proposal(self, Xd):
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def _local_discretization(self, kwargs):
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"""Discretize each feature with its fathers and the class"""
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res = Xd.copy()
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res = self.Xd.copy()
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upgraded = False
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for idx, feature in enumerate(self.feature_names_in_):
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fathers = self.dag_.get_parents(feature)
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for idx, feature in enumerate(self.estimator.feature_names_in_):
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fathers = self.estimator.dag_.get_parents(feature)
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if len(fathers) > 1:
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# First remove the class name as it will be added later
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fathers.remove(self.class_name_)
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fathers.remove(self.estimator.class_name_)
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# Get the fathers indices
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features = [self.idx_features_[f] for f in fathers]
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# Update the discretization of the feature
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res[:, idx] = self.discretizer_.join_fit(
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target=idx, features=features, data=Xd
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target=idx, features=features, data=self.Xd
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)
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upgraded = True
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if upgraded:
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self.compute_kwargs(res, self.y_, self.kwargs_)
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super().fit(res, self.y_, **self.kwargs_)
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kwargs = self.compute_kwargs(res, self.estimator.y_, kwargs)
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return upgraded, res, kwargs
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