How do i create Confusion matrix of predicted and ground truth labels with Tensorflow?

Question

I have implemented a Nueral Network model for a classification with the help of using TensorFlow. But, i don\'t know how can i able to draw confusion matrix by using predic

Answer 1

If you want to produce a confusion matrix, and then later precision and recall, you first need to get your counts of true positives, true negatives, false positives and false negatives. Here is how:

For better readibility, I wrote the code very verbose.

def evaluation(logits,labels):
"Returns correct predictions, and 4 values needed for precision, recall and F1 score"


    # Step 1:
    # Let's create 2 vectors that will contain boolean values, and will describe our labels

    is_label_one = tf.cast(labels, dtype=tf.bool)
    is_label_zero = tf.logical_not(is_label_one)
    # Imagine that labels = [0,1]
    # Then
    # is_label_one = [False,True]
    # is_label_zero = [True,False]

    # Step 2:
    # get the prediction and false prediction vectors. correct_prediction is something that you choose within your model.
    correct_prediction = tf.nn.in_top_k(logits, labels, 1, name="correct_answers")
    false_prediction = tf.logical_not(correct_prediction)

    # Step 3:
    # get the 4 metrics by comparing boolean vectors
    # TRUE POSITIVES
    true_positives = tf.reduce_sum(tf.to_int32(tf.logical_and(correct_prediction,is_label_one)))

    # FALSE POSITIVES
    false_positives = tf.reduce_sum(tf.to_int32(tf.logical_and(false_prediction, is_label_zero)))

    # TRUE NEGATIVES
    true_negatives = tf.reduce_sum(tf.to_int32(tf.logical_and(correct_prediction, is_label_zero)))

    # FALSE NEGATIVES
    false_negatives = tf.reduce_sum(tf.to_int32(tf.logical_and(false_prediction, is_label_one)))


return true_positives, false_positives, true_negatives, false_negatives

# Now you can do something like this in your session:

true_positives, \
false_positives, \
true_negatives, \
false_negatives = sess.run(evaluation(logits,labels), feed_dict=feed_dict)

# you can print the confusion matrix using the 4 values from above, or get precision and recall:
precision = float(true_positives) / float(true_positives+false_positives)
recall = float(true_positives) / float(true_positives+false_negatives)

# or F1 score:
F1_score = 2 * ( precision * recall ) / ( precision+recall )

Answer 2

For the moment, I use this solution to obtain the confusion matrix :

# load the data
(train_x, train_y), (dev_x, dev_y), (test_x, test_y) = dataLoader.load()

# build the classifier
classifier = tf.estimator.DNNClassifier(...)

# train the classifier
classifier.train(input_fn=lambda:train_input_fn(), steps=1000)

# evaluate and prediction on the test set
test_evaluate = classifier.evaluate(input_fn=lambda:eval_input_fn())
test_predict = classifier.predict(input_fn = lambda:eval_input_fn())

# parse the prediction to retrieve the predicted labels
predictions = []

for i in list(test_predict):
    predictions.append(i['class_ids'][0])

# build the prediction matrix
matrix = tf.confusion_matrix(test_y, predictions)

#display the prediction matrix
with tf.Session():
    print(str(tf.Tensor.eval(matrix)))

But I am not convince by my loop to retrieve the predicted labels...there should be a better Python way to do this...(or a TensorFlow way...)

Answer 3

This code worked for me. I sort it out myself :)

from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.metrics import classification_report

def print_confusion_matrix(plabels,tlabels):
"""
    functions print the confusion matrix for the different classes
    to find the error...

    Input:
    -----------
    plabels: predicted labels for the classes...
    tlabels: true labels for the classes

    code from: http://stackoverflow.com/questions/2148543/how-to-write-a-confusion-matrix-in-python
"""
import pandas as pd
plabels = pd.Series(plabels)
tlabels = pd.Series(tlabels)

# draw a cross tabulation...
df_confusion = pd.crosstab(tlabels,plabels, rownames=['Actual'], colnames=['Predicted'], margins=True)

#print df_confusion
return df_confusion

def confusionMatrix(text,Labels,y_pred, not_partial):
    y_actu = np.where(Labels[:]==1)[1]
    df = print_confusion_matrix(y_pred,y_actu)
    print "\n",df
    #print plt.imshow(df.as_matrix())
    if not_partial:
       print "\n",classification_report(y_actu, y_pred)
    print "\n\t------------------------------------------------------\n"

def do_eval(message, sess, correct_prediction, accuracy, pred, X_, y_,x,y):
    predictions = sess.run([correct_prediction], feed_dict={x: X_, y: y_})
    prediction  = tf.argmax(pred,1)
    labels = prediction.eval(feed_dict={x: X_, y: y_}, session=sess)
    print message, accuracy.eval({x: X_, y: y_}),"\n"
    confusionMatrix("Partial Confusion matrix",y_,predictions[0], False)#Partial confusion Matrix
    confusionMatrix("Complete Confusion matrix",y_,labels, True) #complete confusion Matrix

# Launch the graph
with tf.Session() as sess:
sess.run(init)
data = zip(X_train,y_train)
data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int(len(data)/batch_size) + 1
for epoch in range(training_epochs):
    avg_cost = 0.
    # Shuffle the data at each epoch
    shuffle_indices = np.random.permutation(np.arange(data_size))
    shuffled_data = data[shuffle_indices]
    for batch_num in range(num_batches_per_epoch):
        start_index = batch_num * batch_size
        end_index = min((batch_num + 1) * batch_size, data_size)
        sample = zip(*shuffled_data[start_index:end_index])
        #picking up random batches from training set of specific size
        batch_xs, batch_ys = sample[0],sample[1]
        # Fit training using batch data
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
        # Compute average loss
        avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/num_batches_per_epoch
    #append loss
    loss_history.append(avg_cost)

    # Display logs per epoch step
    if (epoch % display_step == 0):           
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))            
        # Calculate training  accuracy
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        trainAccuracy = accuracy.eval({x: X_train, y: y_train})
        train_acc_history.append(trainAccuracy)           
        # Calculate validation  accuracy
        valAccuracy = accuracy.eval({x: X_val, y: y_val})
        val_acc_history.append(valAccuracy) 
        print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost), "train=",trainAccuracy,"val=", valAccuracy

print "Optimization Finished!\n"

# Evaluation of  model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

do_eval("Accuracy of Gold Test set Results: ", sess, correct_prediction, accuracy, pred, X_gold, y_gold, x, y)

and here is the sample output:

Accuracy of Gold Test set Results:  0.642608 


Predicted  False  True  All
Actual                     
0             20    46   66
1              3     1    4
2             21     1   22
3              8     4   12
4             16     7   23
5             54   259  313
6             41    14   55
7             11     2   13
8             48    94  142
9             29     4   33
10            17     4   21
11            39   116  155
All          307   552  859

Predicted   0  1  2   3   4    5   6   7    8   9  10   11  All
Actual                                                         
0          46  0  0   0   0    8   0   2    2   2   0    6   66
1           0  1  0   1   0    2   0   0    0   0   0    0    4
2           3  0  1   3   0   12   0   0    1   0   0    2   22
3           2  0  0   4   1    3   1   1    0   0   0    0   12
4           1  0  0   0   7   12   0   0    1   0   0    2   23
5           8  0  0   1   5  259   9   0    9   3   1   18  313
6           1  0  0   1   6   30  14   1    2   0   0    0   55
7           3  0  0   0   0    2   0   2    4   0   1    1   13
8           6  0  0   1   1   18   0   3   94   8   1   10  142
9           9  0  0   0   0    1   1   1    9   4   0    8   33
10          1  0  0   0   3    6   0   1    1   0   4    5   21
11          5  1  0   1   0   18   1   0    6   5   2  116  155
All        85  2  1  12  23  371  26  11  129  22   9  168  859

         precision    recall  f1-score   support

      0       0.54      0.70      0.61        66
      1       0.50      0.25      0.33         4
      2       1.00      0.05      0.09        22
      3       0.33      0.33      0.33        12
      4       0.30      0.30      0.30        23
      5       0.70      0.83      0.76       313
      6       0.54      0.25      0.35        55
      7       0.18      0.15      0.17        13
      8       0.73      0.66      0.69       142
      9       0.18      0.12      0.15        33
     10       0.44      0.19      0.27        21
     11       0.69      0.75      0.72       155

     avg / total       0.64      0.64      0.62       859