roc curve with sklearn [python]
<p>I have an understanding problem by using the roc libraries.</p>

<p>I want to plot a roc curve with a python
<a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html</a></p>

<p>I am writing a program which evalutes detectors (haarcascade, neuronal networks) and want to evaluate them. 
So I already have the data saved in a file in the following format:</p>

<pre><code> 0.5 TP
 0.43 FP
 0.72 FN
 0.82 TN 
 ...
</code></pre>

<p>whereas TP means True Positive, FP - False Positivve, FN - False Negative, TN - True Negative</p>

<p>I parse it and fill 4 arrays with this data set. </p>

<p>Then I want to put this in </p>

<pre><code>   fpr, tpr = sklearn.metrics.roc_curve(y_true, y_score, average='macro', sample_weight=None)
</code></pre>

<p>but how to do this? What is y_true in my case and y_score?
afterwards, I put it fpr, tpr in</p>

<pre><code>auc = sklearn.metric.auc(fpr, tpr)
</code></pre>