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This issue has been mentioned a few times here on Stackoverflow, but none provided the solution for the problem/error I'm currently facing.

Currently my y of the dataset that I use as labels had to be transformed using One-Hot Encoding so that my Deep Learning network/model could handle it as a categorical_crossentropy.

But now the problem arises that for the evaluation of my data, it needs the original labels again for the prediction of y.

import pandas as pd
import numpy as np

keypoints = pd.read_csv('keypoints.csv')

X = keypoints.iloc[:,1:76]
y = keypoints.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, stratify=y)

Here y is a list of 3 different labels, let's say A,B and C with a shape of (63564, 1)

So using the One-Hot encoding I was able to split it up:

le = LabelEncoder()
y = le.fit_transform(y)
ohe = OneHotEncoder(categorical_features = [0])
y = ohe.fit_transform(y[:,None]).toarray()

The new y here has a shape of (63564, 3) and looks like:

[[0. 0. 1.]
 [0. 0. 1.]
 [0. 0. 1.]
 ...
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]]

After running my Deep Learning network I want to evaluate it by using:

......
#Evaluation and such
y_pred = model.predict(X_test, verbose=0)
y_classes = model.predict_classes(X_test, verbose=0)

#Reduce to 1D
y_pred = y_pred[:, 0]
y_classes = y_classes[:, 0]

#Confution Matrix
print(confusion_matrix(y_test, y_classes))

#Accuracy: (tp + tn) / (p + n)
accuracy = accuracy_score(y_test, y_classes)
print('Accuracy: %f' % accuracy)
#Precision tp / (tp + fp)
precision = precision_score(y_test, y_classes)
print('Precision: %f' % precision)
#Recall: tp / (tp + fn)
recall = recall_score(y_test, y_classes)
print('Recall: %f' % recall)
#F1: 2 tp / (2 tp + fp + fn)
f1 = f1_score(y_test, y_classes)
print('F1 score: %f' % f1)

But ofcourse this won't accept the 0 and 1 as labels:

ValueError: Classification metrics can't handle a mix of unknown and continuous-multioutput targets

So my question is

How do i reverese the One-Hot Encoded labels so that I can run the evaluation of my DL model?

JeBo
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3 Answers3

2

You probably will need inverse_transform as documented in the examples section of sklearn.preprocessing.OneHotEncoder

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder(handle_unknown='ignore')
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.transform([['Female', 1], ['Male', 4]]).toarray()
array([[1., 0., 1., 0., 0.],
       [0., 1., 0., 0., 0.]])
>>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])
array([['Male', 1],
       [None, 2]], dtype=object)
Drey
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    Thanks for your reply, unfortunately I'm unable to get this to work. Because i apply my One-Hot Encoding on my *y* before its fed to the the *train_test_split* and when I give my *y* back to the *inverse_transform* the dimensions for the confusion matrix do not match: `ValueError: Found input variables with inconsistent numbers of samples: [12713, 63564]` And giving the *inverse_transform* the *y_test* as input gives the following error: `ValueError: Found array with dim 3. Estimator expected <= 2.` – JeBo Aug 10 '19 at 22:11
1

You can use argmax to convert probabilities to categorical decisions:

y_test_classes = y_test.argmax(1)
y_pred_classes = y_pred.argmax(1)

print(confusion_matrix(y_true=y_test_classes, y_pred=y_pred_classes, labels=['A', 'B', 'C']))
Mehdi
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  • Thanks for your reply, but I'm kind of confused by the naming of the first two lines since they are not reused in the print confusion matrix line? Because I still get the same error when applying the above. – JeBo Aug 10 '19 at 21:53
  • Thanks for the edit, unfortunately I still get an error **ValueError: Classification metrics can't handle a mix of multilabel-indicator and multiclass targets.** - The output of *y_test_classes* and *y_pred_classes* is as follows: *y_test_classes* --> `[[0 0 0] [0 0 0] [0 0 0] ... [0 0 0] [0 0 0] [0 0 0]]` - *y_test_classes.shape* --> `(12713, 3)` - *y_pred_classes* --> `[2 2 0 ... 0 0 2]` y_pred_classes.shape --> `(12713,)` – JeBo Aug 11 '19 at 06:49
0
y_classes = np.argmax(y_test_4, axis=1)


y_classes = model_4.predict_classes(x_test_4, verbose=0)
y_classes =  np.reshape(yhat_classes_4, (-1, 1))

y_classes= y_classes[:, 0]
 
# accuracy: (tp + tn) / (p + n)
accuracy_4 = accuracy_score(y_test_classes_4, y_classes)
print('Accuracy: %f' % accuracy_4)
# precision tp / (tp + fp)
precision_4 = precision_score(y_test_classes_4, y_classes)
print('Precision: %f' % precision_4)
# recall: tp / (tp + fn)
recall_4 = recall_score(y_test_classes_4, y_classes)
print('Recall: %f' % recall_4)
# f1: 2 tp / (2 tp + fp + fn)
f1_4 = f1_score(y_test_classes_4, y_classes)
print('F1 score: %f' % f1_4)

Use this. You want to first convert your one hot encoded data using argmax function to compare your predictions.' Coming to the predictions part , you want to convert your 1D array to a 2D array as stated in line 3

Rohan Mathur
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