Find maximal element in numpy ndarray for each sub array

Question

I have a ndarray that includes probabilities for being associated to specific class. This is multi-class problem in which every record can be associated to class 0 - 4.

I used one of the classifiers of sckit-learn:

classifier = RandomForestClassifier(n_estimators=100)
predictions_proba = classifier.predict_proba(dataframe)

Let's look on predictions_proba

array([[ 0.2       ,  0.36      ,  0.32      ,  0.05      ,  0.07      ],
   [ 0.04      ,  0.54      ,  0.29      ,  0.08      ,  0.05      ],
   [ 0.05      ,  0.02      ,  0.        ,  0.93      ,  0.        ],
   ..., 
   [ 0.47777778,  0.2       ,  0.13      ,  0.19      ,  0.00222222],
   [ 0.5951746 ,  0.        ,  0.        ,  0.        ,  0.4048254 ],
   [ 0.        ,  0.        ,  0.        ,  0.13837252,  0.86162748]])

I would like to find the easiest way for finding the greatest probability in each sub array. For the example above I would like to return:

[ 0.36, 0.54, 0.93,..., 0.86162748]

0.36 is the greatest probability in the first array, 0.54 is the greatest probility in the second array, and so on.

Yeah. I didn't understand that I have to put the ndarray as a parameter to np.array. — Aviade
– Aviade, Commented Nov 17, 2016 at 20:11

Julien Spronck · Accepted Answer · 2016-11-17 20:07:21Z

1

arr = np.array([[1,5],[7,3]])
# array([[1, 5],
#        [7, 3]])
arr.max(axis=1)
# array([5, 7])

answered Nov 17, 2016 at 20:07

Julien Spronck

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