scikit-learn画ROC图
Posted ck85
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了scikit-learn画ROC图相关的知识,希望对你有一定的参考价值。
1.使用sklearn库和matplotlib.pyplot库
import sklearn import matplotlib.pyplot as plt
2.准备绘图函数的传入参数1.预测的概率值数组2.预测的labels值数组
for i in range(len(y_labeles)): a = np.argmax(y_labeles[i]) y_pred.append(y_conv.eval(feed_dict={x: np.reshape(mnist.test.images[i], [1, 784]), keep_prob: 0.5}, session=sess)[0][a]) y_labeles_d1.append(correct_prediction.eval(feed_dict={x: np.reshape(mnist.test.images[i], [1, 784]), y_: np.reshape(y_labeles[i], [1, 10]), keep_prob: 0.5}, session=sess))
3.调用sklearn.metrics.roc_curve();
fpr, tpr, thresholds = sklearn.metrics.roc_curve(y_labeles_d1, y_pred) plt.plot(fpr, tpr, ‘b‘)#生成ROC曲线 plt.legend(loc=‘lower right‘) plt.plot([0, 1], [0, 1], ‘r--‘) plt.xlim([0, 1]) plt.ylim([0, 1]) plt.ylabel(‘TP‘) plt.xlabel(‘FP‘) plt.show()
4.例子
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np import sklearn import matplotlib.pyplot as plt mnist = input_data.read_data_sets(‘data/‘, one_hot=True) def weight_variable(shape, name): initial = tf.truncated_normal(shape, stddev=0.1, name=name) return tf.Variable(initial) def bias_variable(shape, name): initial = tf.constant(0.1, shape=shape, name=name) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=‘SAME‘) def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘) x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 10]) x_image = tf.reshape(x, [-1, 28, 28, 1]) W_conv1 = weight_variable([5, 5, 1, 32], name=‘W_conv1‘) b_conv1 = bias_variable([32], name=‘b_conv1‘) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64], name=‘W_conv2‘) b_conv2 = bias_variable([64], name=‘b_conv2‘) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_variable([7*7*64, 1024], name=‘W_fc1‘) b_fc1 = bias_variable([1024], name=‘b_fc1‘) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10], name=‘W_fc2‘) b_fc2 = bias_variable([10], name=‘b_fc2‘) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) cross_entropy = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y_)) # cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1])) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) sess = tf.Session() sess.run(tf.global_variables_initializer()) for i in range(500): batch = mnist.train.next_batch(100) train_step.run(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) if i % 500 == 0 and i != 0: train_accuracy = accuracy.eval(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) print(‘step %d, training accuracy %g‘ % (i, train_accuracy)) print("!!!!!") print(‘test accuracy %g‘ % accuracy.eval(session=sess, feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0 })) saver = tf.train.Saver() saver.save(sess, ‘./trained_variables.ckpt‘, global_step=1000) y_labeles = mnist.test.labels y_pred = [] y_labeles_d1 = [] for i in range(len(y_labeles)): a = np.argmax(y_labeles[i]) y_pred.append(y_conv.eval(feed_dict={x: np.reshape(mnist.test.images[i], [1, 784]), keep_prob: 0.5}, session=sess)[0][a]) y_labeles_d1.append(correct_prediction.eval(feed_dict={x: np.reshape(mnist.test.images[i], [1, 784]), y_: np.reshape(y_labeles[i], [1, 10]), keep_prob: 0.5}, session=sess)) fpr, tpr, thresholds = sklearn.metrics.roc_curve(y_labeles_d1, y_pred) plt.plot(fpr, tpr, ‘b‘)#生成ROC曲线 plt.legend(loc=‘lower right‘) plt.plot([0, 1], [0, 1], ‘r--‘) plt.xlim([0, 1]) plt.ylim([0, 1]) plt.ylabel(‘TP‘) plt.xlabel(‘FP‘) plt.show() # with tf.Session() as sess: # new_saver = tf.train.import_meta_graph(‘my_test_model-1000.meta‘) # new_saver.restore(sess, tf.train.latest_checkpoint(‘./‘)) # print(sess.run(W_conv1))
5.效果:
以上是关于scikit-learn画ROC图的主要内容,如果未能解决你的问题,请参考以下文章
VotingClassifier 中的 roc_auc,scikit-learn (sklearn) 中的 RandomForestClassifier
Scikit-learn 中 Kfold 的 ROC 曲线。对 StratifiedKfold 有效,但对 Kfold 显示错误
如何使用 Tensorflow 和 scikit-learn 绘制 ROC 曲线?
IndexError:使用 scikit-learn 绘制 ROC 曲线时数组索引过多?
StatsModels 的 predict 函数如何与 scikit-learn 的 roc_auc_score 交互?