glotzerlab / dupin Goto Github PK

import dupin as du
import sklearn as sk
import numpy as np

data = [4,5,8,1,4,2,6,8,23,5432,76,23,4,2,6,7,3,4,6,3,4,67,3,4,6,3,4,6,6,3,5,7,45,2,5]
decision_tree = sk.tree.DecisionTreeClassifier(max_depth=1)

window = du.preprocessing.supervised.Window(
    classifier=decision_tree, window_size=10, test_size=0.5,
    n_classifiers=50, combine_errors="mean"
)

data = np.asarray(data).reshape(-1,1)
errors = window.compute(data)

File ~/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py:215, in Window.compute(self, X)
    [213](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=212)     self.classifiers_ = []
    [214](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=213) y = np.repeat([0, 1], np.ceil(self.window_size / 2))[: self.window_size]
--> [215](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=214) if isinstance(X, "pandas.core.frame.DataFrame"):
    [216](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=215)     X = X.to_numpy()
    [217](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=216) shuffle_splits = sk.model_selection.StratifiedShuffleSplit(
    [218](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=217)     n_splits=self.n_classifiers, test_size=self.test_size
    [219](file:///home/domi/CODE/GLOTZERLAB/dupin/dupin/preprocessing/supervised.py?line=218) )

TypeError: isinstance() arg 2 must be a type, a tuple of types, or a union

import numpy as np
import dupin as du
import ruptures as rpt
dl=np.asarray([1,1,1,1,1,1,0,0,0,0])
#dl = du.preprocessing.signal.moving_average(dl,3)
lin_regress_cost = du.detect.offline.CostLinearBiasedFit()
dynp = rpt.Dynp(custom_cost=lin_regress_cost)
sweep_detector = du.detect.offline.SweepDetector(dynp, max_change_points=6)
change_points = sweep_detector.fit(dl)