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Update rotation_forest.py #3

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60a95d6
Update rotation_forest.py
cugfzx Apr 18, 2022
7f37e26
Update rotation_forest.py
cugfzx Apr 19, 2022
95bf36f
fixed a essential but hidden mistake about math
cugfzx Apr 26, 2022
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21 changes: 10 additions & 11 deletions rotation_forest/rotation_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

from sklearn.tree import DecisionTreeClassifier
from sklearn.tree._tree import DTYPE
from sklearn.ensemble.forest import ForestClassifier
from sklearn.ensemble._forest import ForestClassifier
from sklearn.utils import resample, gen_batches, check_random_state
from sklearn.utils.extmath import fast_dot
from sklearn.decomposition import PCA, RandomizedPCA
Expand All @@ -12,7 +12,7 @@
def random_feature_subsets(array, batch_size, random_state=1234):
""" Generate K subsets of the features in X """
random_state = check_random_state(random_state)
features = range(array.shape[1])
features = list(range(array.shape[1]))
random_state.shuffle(features)
for batch in gen_batches(len(features), batch_size):
yield features[batch]
Expand Down Expand Up @@ -71,15 +71,14 @@ def _fit_rotation_matrix(self, X):
n_samples, n_features = X.shape
self.rotation_matrix = np.zeros((n_features, n_features),
dtype=np.float32)
for i, subset in enumerate(
random_feature_subsets(X, self.n_features_per_subset,
random_state=self.random_state)):
# take a 75% bootstrap from the rows
x_sample = resample(X, n_samples=int(n_samples*0.75),
random_state=10*i)
pca = self.pca_algorithm()
pca.fit(x_sample[:, subset])
self.rotation_matrix[np.ix_(subset, subset)] = pca.components_

random_feature=random_feature_subsets(X, self.n_features_per_subset,random_state=self.random_state)
subset=next(random_feature)
# take a 75% bootstrap from the rows
x_sample = resample(X, n_samples=int(n_samples*0.75),random_state=10)
pca = self.pca_algorithm()
pca.fit(x_sample[:, subset])
self.rotation_matrix[np.ix_(subset, subset)] = pca.components_.T #The pca.components_ is the V that linalg.svd() returns and a row-vector,so it should be transposed

def fit(self, X, y, sample_weight=None, check_input=True):
self._fit_rotation_matrix(X)
Expand Down