[FIXED] AxisError: axis -1 is out of bounds

Issue

I already referred the posts here,here and here

Am trying to run a lassoCV model and fit it on my training dataset.

So, I tried the below code (this works)

from numpy import arange
from sklearn.linear_model import LassoCV
from sklearn.model_selection import RepeatedKFold
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=5, random_state=1)
# define model
model = LassoCV(alphas=arange(0, 1, 0.01), cv=cv)
# fit model
model.fit(X_train, y_train)
# summarize chosen configuration
print('alpha: %f' % model.alpha_)  # returns 0.78 as best value
y_pred = model.predict(X_test)

However, when I try the below in next cell of jupyter notebook, I get an error

# define model
model_new = LassoCV(alphas=0.78)
# fit model
model_new.fit(X_train,pd.DataFrame(y_train))  # I also tried just X_train and y_train but still doesn't work

AxisError                                 Traceback (most recent call last) Input In [136], in <cell line: 4>()
      2 model_new = LassoCV(alphas=0.78)
      3 # fit model
----> 4 model_new.fit(X_train,pd.DataFrame(y_train))

File ~\Anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:1687, in LinearModelCV.fit(self, X, y, sample_weight)    1685             check_scalar_alpha(alpha, f"alphas[{index}]")    1686     # Making sure alphas is properly ordered.
-> 1687     alphas = np.tile(np.sort(alphas)[::-1], (n_l1_ratio, 1))    1689 # We want n_alphas to be the number of alphas used for each l1_ratio.    1690 n_alphas = len(alphas[0])

File <__array_function__ internals>:5, in sort(*args, **kwargs)

File ~\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:998, in sort(a, axis, kind, order)
    996 else:
    997     a = asanyarray(a).copy(order="K")
--> 998 a.sort(axis=axis, kind=kind, order=order)
    999 return a

AxisError: axis -1 is out of bounds for array of dimension 0

My shape of X_train and y_train looks like below

updated post

Solution

Try this code, give alpha in nump array

reg = LassoCV(alphas=np.array([0.5]), cv=5, random_state=0).fit(x_train, y_train)

Answered By - Mohammad Ahmed