[FIXED] Why does random.seed( ) not work in generating dataset?

Issue

I'm creating dataset for testing with

import random
from sklearn.datasets import make_regression

random.seed(10)
X, y = make_regression(n_samples = 1000, n_features = 10)
X[0:2]

Could you please explain why I get a different dataset after each running? For example, 2 runs return

array([[-0.28058959, -0.00570283,  0.31728106,  0.52745066,  1.69651572,
        -0.37038286,  0.67825801, -0.71782482, -0.29886242,  0.07891646],
       [ 0.73872413, -0.27472164, -1.70298606, -0.59211593,  0.04060707,
         1.39661574, -1.25656819, -0.79698442, -0.38533316,  0.65484856]])

and

array([[ 0.12493586,  1.01388974,  1.2390685 , -0.13797227,  0.60029193,
        -1.39268898, -0.49804303,  1.31267837,  0.11774784,  0.56224193],
       [ 0.47067323,  0.3845262 ,  1.22959284, -0.02913909, -1.56481745,
        -1.56479078,  2.04082295, -0.22561445, -0.37150552,  0.91750366]])

Solution

You need to put the seed into the make_regression call as parameter:

sklearn.datasets.make_regression(n_samples=100, n_features=100, n_informative=10,
                                 n_targets=1, bias=0.0, effective_rank=None,
                                 tail_strength=0.5, noise=0.0, shuffle=True,
                                 coef=False, random_state= None )
                                             ^°^°^°^°^°^°^°^°^°

See API:

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

So in your case:

X, y = make_regression(n_samples = 1000, n_features = 10, random_state = 10)

Answered By - Patrick Artner