Convert NumPy array to 0 or 1 based on threshold
Question:
I have an array below:
a=np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9])
What I want is to convert this vector to a binary vector based on a threshold.
take threshold=0.5 as an example, element that greater than 0.5 convert to 1, otherwise 0.
The output vector should like this:
a_output = [0, 0, 0, 1, 1, 1]
How can I do this?
Answers:
np.where
np.where(a > 0.5, 1, 0)
# array([0, 0, 0, 1, 1, 1])
Boolean basking with astype
(a > .5).astype(int)
# array([0, 0, 0, 1, 1, 1])
np.select
np.select([a <= .5, a>.5], [np.zeros_like(a), np.ones_like(a)])
# array([ 0., 0., 0., 1., 1., 1.])
Special case: np.round
This is the best solution if your array values are floating values between 0 and 1 and your threshold is 0.5.
a.round()
# array([0., 0., 0., 1., 1., 1.])
You could use binarize from the sklearn.preprocessing module.
However this will work only if you want your final values to be binary i.e. ‘0’ or ‘1’. The answers provided above are great of non-binary results as well.
from sklearn.preprocessing import binarize
a = np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9]).reshape(1,-1)
x = binarize(a)
a_output = np.ravel(x)
print(a_output)
#everything together
a_output = np.ravel(binarize(a.reshape(1,-1), 0.5))
I have an array below:
a=np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9])
What I want is to convert this vector to a binary vector based on a threshold.
take threshold=0.5 as an example, element that greater than 0.5 convert to 1, otherwise 0.
The output vector should like this:
a_output = [0, 0, 0, 1, 1, 1]
How can I do this?
np.where
np.where(a > 0.5, 1, 0)
# array([0, 0, 0, 1, 1, 1])
Boolean basking with astype
(a > .5).astype(int)
# array([0, 0, 0, 1, 1, 1])
np.select
np.select([a <= .5, a>.5], [np.zeros_like(a), np.ones_like(a)])
# array([ 0., 0., 0., 1., 1., 1.])
Special case: np.round
This is the best solution if your array values are floating values between 0 and 1 and your threshold is 0.5.
a.round()
# array([0., 0., 0., 1., 1., 1.])
You could use binarize from the sklearn.preprocessing module.
However this will work only if you want your final values to be binary i.e. ‘0’ or ‘1’. The answers provided above are great of non-binary results as well.
from sklearn.preprocessing import binarize
a = np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9]).reshape(1,-1)
x = binarize(a)
a_output = np.ravel(x)
print(a_output)
#everything together
a_output = np.ravel(binarize(a.reshape(1,-1), 0.5))