Create dataset of actual tag and score. Sort dataset by decreasing score. For every row, do the following (treat as rough pseudocode):
# calculate these
total.class.1.seen = # number of class-1 cases >= row.score
seen.cases = # number of cases seen till now.
total.class.1 = # total class 1 cases in data
total.class.0 = # total class 0 cases in data.
TPR = total.class.1.seen/total.class.1
FPR = (seen.cases - total.class.1.seen)/total.class.0
Now plot these TPR and FPR values. Essentially what you're doing at every row, is saying that anything which has score >= this score would be classified as class-1.
The above code doesn't do well when you have non-unique scores (As you have it for 0.7 here). Think of how you want to handle such cases. *Hint: for all unique scores, the plot would be a sequence of only vertical or horizontal line-segments, when non-unique, you get slant line segments.