array-like shape (n_samples,) vs [n_samples] in sklearn documents

For the sample_weight, the requirement of its shape is array-like shape (n_samples,), sometimes is array-like shape [n_samples]. Does (n_samples,) means 1d array? and [n_samples] means list? Or they're equivalent to each other? Both forms can be seen here:

1 Answer

You can use a simple example to test this:

import numpy as np from sklearn.naive_bayes import GaussianNB #create some data X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) Y = np.array([1, 1, 1, 2, 2, 2]) #create the model and fit it clf = GaussianNB() clf.fit(X, Y) #check the type of some attributes type(clf.class_prior_) type(clf.class_count_) #check the shapes of these attributes clf.class_prior_.shape clf.class_count_ 

Or more advanced searching:

#verify that it is a numpy nd array and NOT a list isinstance(clf.class_prior_, np.ndarray) isinstance(clf.class_prior_, list) 

Similarly, you can check all the attributes.

Results

numpy.ndarray numpy.ndarray (2,) array([ 3., 3.]) True False 

The results indicate that these atributes are numpy nd arrays.