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add max_features working
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44
odte/Odte.py
44
odte/Odte.py
@ -53,8 +53,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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def _initialize_random(self) -> np.random.mtrand.RandomState:
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if self.random_state is None:
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return np.random.mtrand._rand
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else:
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return np.random.RandomState(self.random_state)
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return np.random.RandomState(self.random_state)
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@staticmethod
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def _initialize_sample_weight(
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@ -62,8 +61,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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) -> np.array:
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if sample_weight is None:
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return np.ones((n_samples,), dtype=np.float64)
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else:
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return sample_weight.copy()
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return sample_weight.copy()
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def _validate_estimator(self):
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"""Check the estimator and set the base_estimator_ attribute."""
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@ -77,10 +75,9 @@ class Odte(BaseEnsemble, ClassifierMixin):
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# Check parameters are Ok.
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if self.n_estimators < 3:
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raise ValueError(
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f"n_estimators must be greater than 3... got (n_estimators=\
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{self.n_estimators:f})"
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f"n_estimators must be greater than 2 but got (n_estimators=\
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{self.n_estimators})"
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)
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# the rest of parameters are checked in estimator
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check_classification_targets(y)
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X, y = check_X_y(X, y)
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sample_weight = _check_sample_weight(
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@ -90,12 +87,13 @@ class Odte(BaseEnsemble, ClassifierMixin):
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# Initialize computed parameters
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# Build the estimator
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self.n_features_in_ = X.shape[1]
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self.n_features = X.shape[1]
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self.n_features_ = X.shape[1]
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self.max_features_ = self._initialize_max_features()
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self._validate_estimator()
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self.classes_, y = np.unique(y, return_inverse=True)
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self.n_classes_ = self.classes_.shape[0]
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self.estimators_ = []
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self.subspaces_ = []
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self._train(X, y, sample_weight)
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return self
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@ -109,16 +107,19 @@ class Odte(BaseEnsemble, ClassifierMixin):
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for _ in range(self.n_estimators):
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# Build clf
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clf = clone(self.base_estimator_)
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# clf.set_params(**self.estimator_params)
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self.estimators_.append(clf)
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# bootstrap
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indices = random_box.randint(0, n_samples, boot_samples)
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# update weights with the chosen samples
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weights_update = np.bincount(indices, minlength=n_samples)
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features = self.get_subspace(X, y)
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features = self._get_random_subspace(X, y)
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self.subspaces_.append(features)
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current_weights = weights * weights_update
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# train the classifier
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clf.fit(X[indices, features], y[indices], current_weights[indices])
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bootstrap = X[indices, :]
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clf.fit(
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bootstrap[:, features], y[indices], current_weights[indices]
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)
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def _get_bootstrap_n_samples(self, n_samples) -> int:
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if self.max_samples is None:
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@ -157,7 +158,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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elif self.max_features is None:
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max_features = self.n_features_
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elif isinstance(self.max_features, int):
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max_features = self.max_features
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max_features = abs(self.max_features)
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else: # float
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if self.max_features > 0.0:
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max_features = max(
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@ -171,7 +172,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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)
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return max_features
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def _get_subspaces_set(
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def _get_random_subspace(
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self, dataset: np.array, labels: np.array
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) -> np.array:
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features = range(dataset.shape[1])
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@ -182,12 +183,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
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else:
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return features_sets[0]
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def get_subspace(self, dataset: np.array, labels: np.array) -> list:
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"""Return the best subspace to build a tree
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"""
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indices = self._get_subspaces_set(dataset, labels)
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return dataset[:, indices], indices
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def predict(self, X: np.array) -> np.array:
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proba = self.predict_proba(X)
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return self.classes_.take((np.argmax(proba, axis=1)), axis=0)
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@ -196,10 +191,15 @@ class Odte(BaseEnsemble, ClassifierMixin):
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check_is_fitted(self, ["estimators_"])
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# Input validation
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X = check_array(X)
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for tree in self.estimators_:
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if self.n_features_ != X.shape[1]:
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raise ValueError("Number of features of the model must "
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"match the input. Model n_features is {0} and "
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"input n_features is {1}."
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"".format(self.n_features_, X.shape[1]))
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for tree, features in zip(self.estimators_, self.subspaces_):
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n_samples = X.shape[0]
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result = np.zeros((n_samples, self.n_classes_))
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predictions = tree.predict(X)
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predictions = tree.predict(X[:, features])
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for i in range(n_samples):
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result[i, predictions[i]] += 1
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return result
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@ -207,8 +207,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
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def score(
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self, X: np.array, y: np.array, sample_weight: np.array = None
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) -> float:
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# todo
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check_is_fitted(self, ["estimators_"])
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check_classification_targets(y)
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X, y = check_X_y(X, y)
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y_pred = self.predict(X).reshape(y.shape)
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@ -37,6 +37,28 @@ class Odte_test(unittest.TestCase):
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computed = tclf._initialize_sample_weight(value, m)
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self.assertListEqual(expected.tolist(), computed.tolist())
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def test_initialize_max_feature(self):
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expected_values = [
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[0, 4, 10, 11],
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[0, 2, 3, 5, 14, 15],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
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[0, 4, 10, 11],
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[0, 4, 10, 11],
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[0, 4, 10, 11],
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]
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X, y = load_dataset(
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random_state=self._random_state, n_features=16, n_samples=10
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)
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for max_features in [4, 0.4, 1.0, None, "auto", "sqrt", "log2"]:
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tclf = Odte(
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random_state=self._random_state, max_features=max_features
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)
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tclf.fit(X, y)
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computed = tclf._get_random_subspace(X, y)
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expected = expected_values.pop(0)
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self.assertListEqual(expected, list(computed))
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def test_initialize_random(self):
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expected = [37, 235, 908]
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tclf = Odte(random_state=self._random_state)
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@ -51,6 +73,13 @@ class Odte_test(unittest.TestCase):
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self.assertGreaterEqual(value, 101)
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self.assertLessEqual(value, 1000)
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def test_bogus_max_features(self):
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values = ["duck", -0.1, 0.0]
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for max_features in values:
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with self.assertRaises(ValueError):
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tclf = Odte(max_features=max_features)
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tclf.fit(*load_dataset(self._random_state))
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def test_bogus_n_estimator(self):
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values = [0, -1, 2]
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for n_estimators in values:
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@ -79,9 +108,7 @@ class Odte_test(unittest.TestCase):
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X, y = load_dataset(self._random_state)
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expected = y
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tclf = Odte(
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random_state=self._random_state,
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max_features=None,
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max_samples=0.1,
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random_state=self._random_state, max_features=1.0, max_samples=0.1,
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)
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tclf.set_params(**dict(base_estimator__kernel="linear",))
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computed = tclf.fit(X, y).predict(X)
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@ -101,8 +128,8 @@ class Odte_test(unittest.TestCase):
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def test_score_splitter_max_features(self):
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X, y = load_dataset(self._random_state, n_features=12, n_samples=150)
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results = [
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0.9866666666666667,
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0.9866666666666667,
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0.6466666666666666,
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0.6466666666666666,
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0.9866666666666667,
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0.9866666666666667,
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]
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