Menu
I was doing Multi-class Classification using Keras.It contained 5 classes of Output. I converted the single class vector to matrix using one hot encoding and made a model. Now to evaluate the model I want to convert back the 5 class probabilistic result back to Single Column.
I am getting this as output in numpy array format
..................0..................1............................2.......................3.............................4
5.35433665e-02 1.72592481e-05 1.49291719e-03 9.44392741e-01
5.53713820e-04
1.97096306e-05 2.08907949e-08 3.11666554e-07 1.40611945e-07
9.99979794e-01
9.99999225e-01 2.42999278e-07 1.58917388e-07 7.84497018e-08
2.85837785e-07
7.09977685e-05 1.02068476e-09 1.38186664e-07 9.99928594e-01
2.73126261e-07
1.29937407e-05 2.49388819e-07 9.99986231e-01 4.76015231e-07
7.39421040e-08
Want to convert this matrix to
[3,4,0,3,2]
909
OneHotEncoder. Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features. The output will be a sparse matrix where each column corresponds to one possible value of one feature.
Reversing the rows of a data frame in pandas can be done in python by invoking the loc() function. The panda's dataframe. loc() attribute accesses a set of rows and columns in the given data frame by either a label or a boolean array.
We can load this using the load_dataset() function: # One-hot encoding a single column from sklearn. preprocessing import OneHotEncoder from seaborn import load_dataset df = load_dataset('penguins') ohe = OneHotEncoder() transformed = ohe. fit_transform(df[['island']]) print(transformed.
It seems like you are looking for np.argmax:
import numpy as np
class_labels = np.argmax(class_prob, axis=1) # assuming you have n-by-5 class_prob
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
