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)
这是示例输出：
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
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