Tensorflow Precision / Recall / F1 score and Confusion matrix
Question:
I would like to know if there is a way to implement the different score function from the scikit learn package like this one :
from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)
into a tensorflow model to get the different score.
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
init = tf.initialize_all_variables()
sess.run(init)
for epoch in xrange(1):
avg_cost = 0.
total_batch = len(train_arrays) / batch_size
for batch in range(total_batch):
train_step.run(feed_dict = {x: train_arrays, y: train_labels})
avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
print "Optimization Finished!"
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print "Accuracy:", batch, accuracy.eval({x: test_arrays, y: test_labels})
Will i have to run the session again to get the prediction ?
Answers:
Maybe this example will speak to you :
pred = multilayer_perceptron(x, weights, biases)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
for epoch in xrange(150):
for i in xrange(total_batch):
train_step.run(feed_dict = {x: train_arrays, y: train_labels})
avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
#metrics
y_p = tf.argmax(pred, 1)
val_accuracy, y_pred = sess.run([accuracy, y_p], feed_dict={x:test_arrays, y:test_label})
print "validation accuracy:", val_accuracy
y_true = np.argmax(test_label,1)
print "Precision", sk.metrics.precision_score(y_true, y_pred)
print "Recall", sk.metrics.recall_score(y_true, y_pred)
print "f1_score", sk.metrics.f1_score(y_true, y_pred)
print "confusion_matrix"
print sk.metrics.confusion_matrix(y_true, y_pred)
fpr, tpr, tresholds = sk.metrics.roc_curve(y_true, y_pred)
You do not really need sklearn to calculate precision/recall/f1 score. You can easily express them in TF-ish way by looking at the formulas:
Now if you have your actual and predicted values as vectors of 0/1, you can calculate TP, TN, FP, FN using tf.count_nonzero:
TP = tf.count_nonzero(predicted * actual)
TN = tf.count_nonzero((predicted - 1) * (actual - 1))
FP = tf.count_nonzero(predicted * (actual - 1))
FN = tf.count_nonzero((predicted - 1) * actual)
Now your metrics are easy to calculate:
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
Use the metrics APIs provided in tf.contrib.metrics, for example:
labels = ...
predictions = ...
accuracy, update_op_acc = tf.contrib.metrics.streaming_accuracy(labels, predictions)
error, update_op_error = tf.contrib.metrics.streaming_mean_absolute_error(labels, predictions)
sess.run(tf.local_variables_initializer())
for batch in range(num_batches):
sess.run([update_op_acc, update_op_error])
accuracy, mean_absolute_error = sess.run([accuracy, mean_absolute_error])
Since i have not enough reputation to add a comment to Salvador Dalis answer this is the way to go:
tf.count_nonzero casts your values into an tf.int64 unless specified otherwise. Using:
argmax_prediction = tf.argmax(prediction, 1)
argmax_y = tf.argmax(y, 1)
TP = tf.count_nonzero(argmax_prediction * argmax_y, dtype=tf.float32)
TN = tf.count_nonzero((argmax_prediction - 1) * (argmax_y - 1), dtype=tf.float32)
FP = tf.count_nonzero(argmax_prediction * (argmax_y - 1), dtype=tf.float32)
FN = tf.count_nonzero((argmax_prediction - 1) * argmax_y, dtype=tf.float32)
is a realy good idea.
Multi-label case
Previous answers do not specify how to handle the multi-label case so here is such a version implementing three types of multi-label f1 score in tensorflow: micro, macro and weighted (as per scikit-learn)
Update (06/06/18): I wrote a blog post about how to compute the streaming multilabel f1 score in case it helps anyone (it’s a longer process, don’t want to overload this answer)
f1s = [0, 0, 0]
y_true = tf.cast(y_true, tf.float64)
y_pred = tf.cast(y_pred, tf.float64)
for i, axis in enumerate([None, 0]):
TP = tf.count_nonzero(y_pred * y_true, axis=axis)
FP = tf.count_nonzero(y_pred * (y_true - 1), axis=axis)
FN = tf.count_nonzero((y_pred - 1) * y_true, axis=axis)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
f1s[i] = tf.reduce_mean(f1)
weights = tf.reduce_sum(y_true, axis=0)
weights /= tf.reduce_sum(weights)
f1s[2] = tf.reduce_sum(f1 * weights)
micro, macro, weighted = f1s
Correctness
def tf_f1_score(y_true, y_pred):
"""Computes 3 different f1 scores, micro macro
weighted.
micro: f1 score accross the classes, as 1
macro: mean of f1 scores per class
weighted: weighted average of f1 scores per class,
weighted from the support of each class
Args:
y_true (Tensor): labels, with shape (batch, num_classes)
y_pred (Tensor): model's predictions, same shape as y_true
Returns:
tuple(Tensor): (micro, macro, weighted)
tuple of the computed f1 scores
"""
f1s = [0, 0, 0]
y_true = tf.cast(y_true, tf.float64)
y_pred = tf.cast(y_pred, tf.float64)
for i, axis in enumerate([None, 0]):
TP = tf.count_nonzero(y_pred * y_true, axis=axis)
FP = tf.count_nonzero(y_pred * (y_true - 1), axis=axis)
FN = tf.count_nonzero((y_pred - 1) * y_true, axis=axis)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
f1s[i] = tf.reduce_mean(f1)
weights = tf.reduce_sum(y_true, axis=0)
weights /= tf.reduce_sum(weights)
f1s[2] = tf.reduce_sum(f1 * weights)
micro, macro, weighted = f1s
return micro, macro, weighted
def compare(nb, dims):
labels = (np.random.randn(nb, dims) > 0.5).astype(int)
predictions = (np.random.randn(nb, dims) > 0.5).astype(int)
stime = time()
mic = f1_score(labels, predictions, average='micro')
mac = f1_score(labels, predictions, average='macro')
wei = f1_score(labels, predictions, average='weighted')
print('sklearn in {:.4f}:n micro: {:.8f}n macro: {:.8f}n weighted: {:.8f}'.format(
time() - stime, mic, mac, wei
))
gtime = time()
tf.reset_default_graph()
y_true = tf.Variable(labels)
y_pred = tf.Variable(predictions)
micro, macro, weighted = tf_f1_score(y_true, y_pred)
with tf.Session() as sess:
tf.global_variables_initializer().run(session=sess)
stime = time()
mic, mac, wei = sess.run([micro, macro, weighted])
print('tensorflow in {:.4f} ({:.4f} with graph time):n micro: {:.8f}n macro: {:.8f}n weighted: {:.8f}'.format(
time() - stime, time()-gtime, mic, mac, wei
))
compare(10 ** 6, 10)
outputs:
>> rows: 10^6 dimensions: 10
sklearn in 2.3939:
micro: 0.30890287
macro: 0.30890275
weighted: 0.30890279
tensorflow in 0.2465 (3.3246 with graph time):
micro: 0.30890287
macro: 0.30890275
weighted: 0.30890279
I would like to know if there is a way to implement the different score function from the scikit learn package like this one :
from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)
into a tensorflow model to get the different score.
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
init = tf.initialize_all_variables()
sess.run(init)
for epoch in xrange(1):
avg_cost = 0.
total_batch = len(train_arrays) / batch_size
for batch in range(total_batch):
train_step.run(feed_dict = {x: train_arrays, y: train_labels})
avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
print "Optimization Finished!"
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print "Accuracy:", batch, accuracy.eval({x: test_arrays, y: test_labels})
Will i have to run the session again to get the prediction ?
Maybe this example will speak to you :
pred = multilayer_perceptron(x, weights, biases)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
for epoch in xrange(150):
for i in xrange(total_batch):
train_step.run(feed_dict = {x: train_arrays, y: train_labels})
avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch
if epoch % display_step == 0:
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
#metrics
y_p = tf.argmax(pred, 1)
val_accuracy, y_pred = sess.run([accuracy, y_p], feed_dict={x:test_arrays, y:test_label})
print "validation accuracy:", val_accuracy
y_true = np.argmax(test_label,1)
print "Precision", sk.metrics.precision_score(y_true, y_pred)
print "Recall", sk.metrics.recall_score(y_true, y_pred)
print "f1_score", sk.metrics.f1_score(y_true, y_pred)
print "confusion_matrix"
print sk.metrics.confusion_matrix(y_true, y_pred)
fpr, tpr, tresholds = sk.metrics.roc_curve(y_true, y_pred)
You do not really need sklearn to calculate precision/recall/f1 score. You can easily express them in TF-ish way by looking at the formulas:
Now if you have your actual and predicted values as vectors of 0/1, you can calculate TP, TN, FP, FN using tf.count_nonzero:
TP = tf.count_nonzero(predicted * actual)
TN = tf.count_nonzero((predicted - 1) * (actual - 1))
FP = tf.count_nonzero(predicted * (actual - 1))
FN = tf.count_nonzero((predicted - 1) * actual)
Now your metrics are easy to calculate:
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
Use the metrics APIs provided in tf.contrib.metrics, for example:
labels = ...
predictions = ...
accuracy, update_op_acc = tf.contrib.metrics.streaming_accuracy(labels, predictions)
error, update_op_error = tf.contrib.metrics.streaming_mean_absolute_error(labels, predictions)
sess.run(tf.local_variables_initializer())
for batch in range(num_batches):
sess.run([update_op_acc, update_op_error])
accuracy, mean_absolute_error = sess.run([accuracy, mean_absolute_error])
Since i have not enough reputation to add a comment to Salvador Dalis answer this is the way to go:
tf.count_nonzero casts your values into an tf.int64 unless specified otherwise. Using:
argmax_prediction = tf.argmax(prediction, 1)
argmax_y = tf.argmax(y, 1)
TP = tf.count_nonzero(argmax_prediction * argmax_y, dtype=tf.float32)
TN = tf.count_nonzero((argmax_prediction - 1) * (argmax_y - 1), dtype=tf.float32)
FP = tf.count_nonzero(argmax_prediction * (argmax_y - 1), dtype=tf.float32)
FN = tf.count_nonzero((argmax_prediction - 1) * argmax_y, dtype=tf.float32)
is a realy good idea.
Multi-label case
Previous answers do not specify how to handle the multi-label case so here is such a version implementing three types of multi-label f1 score in tensorflow: micro, macro and weighted (as per scikit-learn)
Update (06/06/18): I wrote a blog post about how to compute the streaming multilabel f1 score in case it helps anyone (it’s a longer process, don’t want to overload this answer)
f1s = [0, 0, 0]
y_true = tf.cast(y_true, tf.float64)
y_pred = tf.cast(y_pred, tf.float64)
for i, axis in enumerate([None, 0]):
TP = tf.count_nonzero(y_pred * y_true, axis=axis)
FP = tf.count_nonzero(y_pred * (y_true - 1), axis=axis)
FN = tf.count_nonzero((y_pred - 1) * y_true, axis=axis)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
f1s[i] = tf.reduce_mean(f1)
weights = tf.reduce_sum(y_true, axis=0)
weights /= tf.reduce_sum(weights)
f1s[2] = tf.reduce_sum(f1 * weights)
micro, macro, weighted = f1s
Correctness
def tf_f1_score(y_true, y_pred):
"""Computes 3 different f1 scores, micro macro
weighted.
micro: f1 score accross the classes, as 1
macro: mean of f1 scores per class
weighted: weighted average of f1 scores per class,
weighted from the support of each class
Args:
y_true (Tensor): labels, with shape (batch, num_classes)
y_pred (Tensor): model's predictions, same shape as y_true
Returns:
tuple(Tensor): (micro, macro, weighted)
tuple of the computed f1 scores
"""
f1s = [0, 0, 0]
y_true = tf.cast(y_true, tf.float64)
y_pred = tf.cast(y_pred, tf.float64)
for i, axis in enumerate([None, 0]):
TP = tf.count_nonzero(y_pred * y_true, axis=axis)
FP = tf.count_nonzero(y_pred * (y_true - 1), axis=axis)
FN = tf.count_nonzero((y_pred - 1) * y_true, axis=axis)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)
f1s[i] = tf.reduce_mean(f1)
weights = tf.reduce_sum(y_true, axis=0)
weights /= tf.reduce_sum(weights)
f1s[2] = tf.reduce_sum(f1 * weights)
micro, macro, weighted = f1s
return micro, macro, weighted
def compare(nb, dims):
labels = (np.random.randn(nb, dims) > 0.5).astype(int)
predictions = (np.random.randn(nb, dims) > 0.5).astype(int)
stime = time()
mic = f1_score(labels, predictions, average='micro')
mac = f1_score(labels, predictions, average='macro')
wei = f1_score(labels, predictions, average='weighted')
print('sklearn in {:.4f}:n micro: {:.8f}n macro: {:.8f}n weighted: {:.8f}'.format(
time() - stime, mic, mac, wei
))
gtime = time()
tf.reset_default_graph()
y_true = tf.Variable(labels)
y_pred = tf.Variable(predictions)
micro, macro, weighted = tf_f1_score(y_true, y_pred)
with tf.Session() as sess:
tf.global_variables_initializer().run(session=sess)
stime = time()
mic, mac, wei = sess.run([micro, macro, weighted])
print('tensorflow in {:.4f} ({:.4f} with graph time):n micro: {:.8f}n macro: {:.8f}n weighted: {:.8f}'.format(
time() - stime, time()-gtime, mic, mac, wei
))
compare(10 ** 6, 10)
outputs:
>> rows: 10^6 dimensions: 10
sklearn in 2.3939:
micro: 0.30890287
macro: 0.30890275
weighted: 0.30890279
tensorflow in 0.2465 (3.3246 with graph time):
micro: 0.30890287
macro: 0.30890275
weighted: 0.30890279