add eda/ml_tools/roc_curve.py

eda/ml_tools/roc_curve.py

+import pandas as pd
+from sklearn import metrics
+import matplotlib.pyplot as plt
+from sklearn.metrics import auc
+import argparse
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('test_label',help='The filename of the test_label')
+parser.add_argument('test_pred',help='The filename of the test_pred')
+parser.add_argument('output_photo',help='The filename of the output_photo')
+args = parser.parse_args()
+
+
+def get_roc_curve(label,pred,output):
+	"""
+	计算二分类问题的roc和auc
+	"""
+	try:
+		test_label = pd.read_table(label)
+		pred_label = pd.read_table(pred)
+		y = test_label.values
+		p = pred_label.values
+		fpr, tpr, thresholds = metrics.roc_curve(y, p)
+		plt.plot(fpr,tpr,marker = 'o')
+		plt.xlabel('False positive rate')
+		plt.ylabel('True positive rate')
+		plt.title('roc_cureve')
+		AUC = auc(fpr, tpr)
+		AUC = "auc={}".format(AUC)
+		plt.text(0.5,0.8,AUC,color='blue',ha='center')
+		plt.savefig(output)
+		print(AUC)
+	except:
+		print("the format of the file must be the n*1")
+		print("the test_label must be 0 or 1")
+		print("the test_pred must be at [0,1]")
+
+if __name__ == "__main__":
+	get_roc_curve(args.test_label,args.test_pred,args.output_photo)