[19]:
import os
import joblib
import numpy as np
[22]:
model = joblib.load(os.path.join('models', 'model.pkl'))
[33]:
input_ = np.random.normal(0, 1, size=(16, 1000))#.reshape(1, -1)
input_.shape
[33]:
(16, 1000)
[34]:
 model.predict(input_) # 1, 2, 3, 4
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [34], in <module>
----> 1 model.predict(input_)

File ~/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:113, in _AvailableIfDescriptor.__get__.<locals>.<lambda>(*args, **kwargs)
    110         raise attr_err
    112     # lambda, but not partial, allows help() to work with update_wrapper
--> 113     out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa
    114 else:
    116     def fn(*args, **kwargs):

File ~/.local/lib/python3.10/site-packages/sklearn/pipeline.py:469, in Pipeline.predict(self, X, **predict_params)
    467 Xt = X
    468 for _, name, transform in self._iter(with_final=False):
--> 469     Xt = transform.transform(Xt)
    470 return self.steps[-1][1].predict(Xt, **predict_params)

File ~/.local/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:993, in StandardScaler.transform(self, X, copy)
    991 else:
    992     if self.with_mean:
--> 993         X -= self.mean_
    994     if self.with_std:
    995         X /= self.scale_

ValueError: operands could not be broadcast together with shapes (16,1000) (44,) (16,1000)
[ ]:

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