import time
import random
from bandit import Bandit
for i in range(250):
print i
time.sleep(0.01)
with open('output-files/stuff.txt', 'wb') as f:
f.write("HI!")
bandit = Bandit()
bandit.metadata.x = 1
bandit.metadata.y2 = 0.83
bandit.metadata.r2 = "hello!"
for x in range(10):
for y in range(10):
for tag in ["a", "b", "c", "d", "e", "f", "g"]:
bandit.report(tag, y, random.normalvariate(0, 1))
time.sleep(0.1)
# email = job.Email("*****@*****.**", "This is a test email", "Hello self!")
# email._write()
import pandas as pd
from bandit import Bandit
import time
bandit = Bandit()
df = pd.read_csv("./report-data.csv")
for _, row in df.iterrows():
row = row.to_dict()
bandit.report(row['tag_name'], row['y'])
time.sleep(0.1)
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# loop over test data
for i in range(len(Xte)):
# Get nearest neighbor
nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
# Get nearest neighbor class label and compare it to its true label
print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
"True Class:", np.argmax(Yte[i]))
# Calculate accuracy
if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
accuracy += 1. / len(Xte)
print('accuracy:', accuracy)
bandit.report('Accuracty', accuracy)
print("Done!")
print("Accuracy:", accuracy)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost],
feed_dict={
x: batch_xs,
y: batch_ys
})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if (epoch + 1) % display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=",
"{:.9f}".format(avg_cost))
bandit.report('Cost', float(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:",
accuracy.eval({
x: mnist.test.images,
y: mnist.test.labels
}))
bandit.metadata.accuracy = float(
accuracy.eval({
x: mnist.test.images,
