尽管有相同数量的案件，但案件数量不匹配

Question

sktime学习分类

    from sklearn.model_selection import train_test_split
X = AUDCHF_h1_model[['Open','High','Low','Close','Volume','VWMA',
                                   'Minute','Hour','Day','Week','Month','Year']].values
y = AUDCHF_h1_model[['is_beg_leg']].values

X_train,X_test,y_train,y_test = train_test_split(
    X, y, test_size=0.2)

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

(53250，12) (53250，1) (13313，12) (13313，1)

    import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline

from sktime.classification.compose import ColumnEnsembleClassifier
from sktime.classification.dictionary_based import BOSSEnsemble
from sktime.classification.interval_based import TimeSeriesForestClassifier
#from sktime.classification.shapelet_based import MrSEQLClassifier
from sktime.datasets import load_basic_motions
from sktime.transformations.panel.compose import ColumnConcatenator

    steps = [
    ("concatenate", ColumnConcatenator()),
    ("classify", TimeSeriesForestClassifier(n_estimators=100)),
]
clf = Pipeline(steps)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)

我收到

ValueError:病例数不匹配。X中的数目= 639000，y= 53250

但

X_train.shape (53250，12) y_train.shape (53250，1)

谁知道呢？

Answer 1

根据您提供的信息，我不能肯定地说什么，但我怀疑问题是管道中的ColumnConcatenator，它堆叠了X的所有列，以创建一个新的单变量时间序列( 53250 * 12 = 639000行)。然后将这个串联的时间序列传递给TimeSeriesForestClassifier，并具有与原始输入不同的形状。根据您的用例，您现在可以删除“串联”步骤，也可以为新创建的单变量时间序列提供目标值。