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First complete boostAODE

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Ricardo Montañana Gómez
2023-06-26 10:09:28 +02:00
parent 9d7e787f6c
commit ea473fc604
1 changed files with 22 additions and 8 deletions
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30
bayesclass/clfs.py
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@@ -819,7 +819,6 @@ class BoostSPODE(BayesBase):
def _train(self, kwargs): def _train(self, kwargs):
states = dict(state_names=kwargs.get("state_names", [])) states = dict(state_names=kwargs.get("state_names", []))
breakpoint()
self.model_ = BayesianNetwork(self.dag_.edges(), show_progress=False) self.model_ = BayesianNetwork(self.dag_.edges(), show_progress=False)
self.model_.fit( self.model_.fit(
self.dataset_, self.dataset_,
@@ -836,11 +835,9 @@ class BoostAODE(ClassifierMixin, BaseEnsemble):
show_progress=False, show_progress=False,
random_state=None, random_state=None,
estimator=None, estimator=None,
n_estimators=10,
): ):
self.show_progress = show_progress self.show_progress = show_progress
self.random_state = random_state self.random_state = random_state
self.n_estimators = n_estimators
super().__init__(estimator=estimator) super().__init__(estimator=estimator)
def _validate_estimator(self) -> None: def _validate_estimator(self) -> None:
@@ -906,14 +903,22 @@ class BoostAODE(ClassifierMixin, BaseEnsemble):
def _train(self, kwargs): def _train(self, kwargs):
"""Build boosted SPODEs""" """Build boosted SPODEs"""
weights = [1 / self.n_samples_] * self.n_samples_ weights = [1 / self.n_samples_] * self.n_samples_
selected_features = []
# Step 0: Set the finish condition # Step 0: Set the finish condition
for num in range(self.n_estimators): for _ in range(self.n_features_in_):
# Step 1: Build ranking with mutual information # Step 1: Build ranking with mutual information
n_feature = ( features = (
CSelectKBestWeighted(self.X_, self.y_, weights, k=1) CSelectKBestWeighted(
self.X_, self.y_, weights, k=self.n_features_in_
)
.fit() .fit()
.get_features()[0] .get_features()
) )
# Step 1.1: Select the feature to become the sparent
for n_feature in features:
if n_feature not in selected_features:
selected_features.append(n_feature)
break
feature = self.feature_names_in_[n_feature] feature = self.feature_names_in_[n_feature]
# Step 2: Build & train spode with the first feature as sparent # Step 2: Build & train spode with the first feature as sparent
estimator = clone(self.estimator_) estimator = clone(self.estimator_)
@@ -921,7 +926,7 @@ class BoostAODE(ClassifierMixin, BaseEnsemble):
_args["sparent"] = feature _args["sparent"] = feature
_args["sample_weight"] = weights _args["sample_weight"] = weights
_args["weighted"] = True _args["weighted"] = True
print("I'm gonna build a spode with", feature) # print("I'm gonna build a spode with", feature)
# Step 2.1: build dataset # Step 2.1: build dataset
# Step 2.2: Train the model # Step 2.2: Train the model
estimator.fit(self.X_, self.y_, **_args) estimator.fit(self.X_, self.y_, **_args)
@@ -937,3 +942,12 @@ class BoostAODE(ClassifierMixin, BaseEnsemble):
] ]
# Step 4: Add the new model # Step 4: Add the new model
self.estimators_.append(estimator) self.estimators_.append(estimator)
self.weights_ = weights
def predict(self, X: np.ndarray) -> np.ndarray:
n_samples = X.shape[0]
n_estimators = len(self.estimators_)
result = np.empty((n_samples, n_estimators))
for index, estimator in enumerate(self.estimators_):
result[:, index] = estimator.predict(X)
return mode(result, axis=1, keepdims=False).mode.ravel()