import itertools
import matplotlib.pyplot as plt
import numpy as np
 
def plot_confusion_matrix(cm, classes,
       normalize=False,
       title='Confusion matrix',
       cmap=plt.cm.Blues):
 """
 This function prints and plots the confusion matrix.
 Normalization can be applied by setting `normalize=True`.
 """
 if normalize:
  cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
  print("Normalized confusion matrix")
 else:
  print('Confusion matrix, without normalization')
 
 print(cm)
 
 plt.imshow(cm, interpolation='nearest', cmap=cmap)
 plt.title(title)
 plt.colorbar()
 tick_marks = np.arange(len(classes))
 plt.xticks(tick_marks, classes, rotation=45)
 plt.yticks(tick_marks, classes)
 
 fmt = '.2f' if normalize else 'd'
 thresh = cm.max() / 2.
 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
  plt.text(j, i, format(cm[i, j], fmt),
     horizontalalignment="center",
     color="white" if cm[i, j] > thresh else "black")
 
 plt.tight_layout()
 plt.ylabel('True label')
 plt.xlabel('Predicted label')
 plt.show()
 # plt.savefig('confusion_matrix',dpi=200)
 
cnf_matrix = np.array([
 [4101, 2, 5, 24, 0],
 [50, 3930, 6, 14, 5],
 [29, 3, 3973, 4, 0],
 [45, 7, 1, 3878, 119],
 [31, 1, 8, 28, 3936],
])
 
class_names = ['Buildings', 'Farmland', 'Greenbelt', 'Wasteland', 'Water']
 
# plt.figure()
# plot_confusion_matrix(cnf_matrix, classes=class_names,
#      title='Confusion matrix, without normalization')
 
# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
      title='Normalized confusion matrix')

skearn.metrics.confusion_matrix(
 y_true, # array, Gound true (correct) target values
 y_pred, # array, Estimated targets as returned by a classifier
 labels=None, # array, List of labels to index the matrix.
 sample_weight=None # array-like of shape = [n_samples], Optional sample weights
)

tf.confusion_matrix(
 labels, # 1-D Tensor of real labels for the classification task
 predictions, # 1-D Tensor of predictions for a givenclassification
 num_classes=None, # The possible number of labels the classification task can have
 dtype=tf.int32, # Data type of the confusion matrix 
 name=None, # Scope name
 weights=None, # An optional Tensor whose shape matches predictions
)

#!/usr/bin/env python
# -*- coding: utf8 -*-
"""
Author: klchang
Description: 
　　A simple example for tf.confusion_matrix and sklearn.metrics.confusion_matrix.
Date: 2018.9.8
"""
from __future__ import print_function
import tensorflow as tf
import sklearn.metrics
 
y_true = [1, 2, 4]
y_pred = [2, 2, 4]
 
# Build graph with tf.confusion_matrix operation
sess = tf.InteractiveSession()
op = tf.confusion_matrix(y_true, y_pred)
op2 = tf.confusion_matrix(y_true, y_pred, num_classes=6, dtype=tf.float32, weights=tf.constant([0.3, 0.4, 0.3]))
# Execute the graph
print ("confusion matrix in tensorflow: ")
print ("1. default: \n", op.eval())
print ("2. customed: \n", sess.run(op2))
sess.close()
 
# Use sklearn.metrics.confusion_matrix function
print ("\nconfusion matrix in scikit-learn: ")
print ("1. default: \n", sklearn.metrics.confusion_matrix(y_true, y_pred))
print ("2. customed: \n", sklearn.metrics.confusion_matrix(y_true, y_pred, labels=range(6), sample_weight=[0.3, 0.4, 0.3]))