I am little bit in trouble finding the value of recall using sklearn. I am using keras 2.0 for solving a binary classification problem. For finding recall I need to depend of sklearn metrics, but I am getting a value error for. Here is my sample code and the error stack:
>> print(y_true[:5])
>> [[ 0. 1.]
[ 0. 1.]
[ 1. 0.]
[ 0. 1.]
[ 1. 0.]]
>> y_scores = model.predict(x_val)
>> print(y_scores[:5])
[[ 7.00690389e-01 2.99309582e-01]
[ 9.36253404e-04 9.99063790e-01]
[ 9.99530196e-01 4.69864986e-04]
[ 6.66563153e-01 3.33436847e-01]
[ 9.98917222e-01 1.08276575e-03]
>>from sklearn import metrics
>>recall_score(y_true, y_scores)
ValueError Traceback (most recent call last)
<ipython-input-39-9f93c0c66265> in <module>()
1 from sklearn import metrics
----> 2 recall_score(y_true, y_scores)
~\AppData\Local\Continuum\miniconda3\envs\deepnetwork\lib\site-
packages\sklearn\metrics\classification.py in recall_score(y_true, y_pred,
labels, pos_label, average, sample_weight)
1357 average=average,
1358 warn_for=('recall',),
-> 1359
sample_weight=sample_weight)
1360 return r
1361
~\AppData\Local\Continuum\miniconda3\envs\deepnetwork\lib\site-
packages\sklearn\metrics\classification.py in
precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label,
average, warn_for, sample_weight)
1023 raise ValueError("beta should be >0 in the F-beta score")
1024
-> 1025 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
1026 present_labels = unique_labels(y_true, y_pred)
1027
~\AppData\Local\Continuum\miniconda3\envs\deepnetwork\lib\site-
packages\sklearn\metrics\classification.py in _check_targets(y_true,
y_pred)
79 if len(y_type) > 1:
80 raise ValueError("Classification metrics can't handle a mix of
{0} "
---> 81 "and {1} targets".format(type_true, type_pred))
82
83 # We can't have more than one value on y_type => The set is no more
needed
ValueError: Classification metrics can't handle a mix of multilabel-
indicator and continuous-multioutput targets
Your y_true and y_scores are one-hot encoded, while, according to the docs, recall_score requires them to be 1-D arrays:
from sklearn.metrics import recall_score
import numpy as np
y_true = [[ 0. , 1.],
[ 0. , 1.],
[ 1. , 0.],
[ 0. , 1.],
[ 1. , 0.]]
y_scores = [[ 7.00690389e-01 , 2.99309582e-01],
[ 9.36253404e-04 , 9.99063790e-01],
[ 9.99530196e-01 , 4.69864986e-04],
[ 6.66563153e-01 , 3.33436847e-01],
[ 9.98917222e-01 , 1.08276575e-03]]
yy_true = [np.argmax(i) for i in y_true]
yy_true
# [1, 1, 0, 1, 0]
yy_scores = [np.argmax(i) for i in y_scores]
yy_scores
# [0, 1, 0, 0, 0]
recall_score(yy_true, yy_scores)
# 0.33333333333333331