import pandas as pd
import numpy as np
import pylab as pl
from sklearn.metrics import roc_curve, auc
df = pd.read_csv('filename.csv')
y_test = np.array(df)[:,0]
probas = np.array(df)[:,1]
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y_test, probas)
roc_auc = auc(fpr, tpr)
print("Area under the ROC curve : %f" % roc_auc)
# Plot ROC curve
pl.clf()
pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
pl.plot([0, 1], [0, 1], 'k--')
pl.xlim([0.0, 1.0])
pl.ylim([0.0, 1.0])
pl.xlabel('False Positive Rate')
pl.ylabel('True Positive Rate')
pl.title('Receiver operating characteristic')
pl.legend(loc="lower right")
pl.show()
RoC curve from csv file
-
05-07-2023 - |
Domanda
How can I use scikit learn or any other python library to draw a roc curve for a csv file such as this:
1, 0.202
0, 0.203
0, 0.266
1, 0.264
0, 0.261
0, 0.291
.......
Soluzione
Altri suggerimenti
Not an answer for python
, but if you use R
(http://www.r-project.org/) it is as easy as
# load data
X <- read.table("mydata.csv", sep = ",")
# create and plot RoC curve
library(ROCR)
roc <- ROCR::performance(ROCR::prediction(X[,2], X[,1]), "tpr", "fpr")
plot(roc)
(you need to install R
package ROCR
beforehand via install.package("ROCR")
)
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