Online Example.py

import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import requests
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Create graph
sess = tf.Session()

# Load the data
housing_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
housing_header = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
cols_used = ['CRIM', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'TAX', 'PTRATIO', 'B', 'LSTAT']
num_features = len(cols_used)
housing_file = requests.get(housing_url)
housing_data = [[float(x) for x in y.split(' ') if len(x)>=1] for y in housing_file.text.split('\n') if len(y)>=1]

y_vals = np.transpose([np.array([y[13] for y in housing_data])])
x_vals = np.array([[x for i,x in enumerate(y) if housing_header[i] in cols_used] for y in housing_data])

## Min-Max Scaling
x_vals = (x_vals - x_vals.min(0)) / x_vals.ptp(0)

# Split the data into train and test sets
np.random.seed(13)  #make results reproducible
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

# Declare k-value and batch size
k = 4
batch_size=len(x_vals_test)

# Placeholders
x_data_train = tf.placeholder(shape=[None, num_features], dtype=tf.float32)
x_data_test = tf.placeholder(shape=[None, num_features], dtype=tf.float32)
y_target_train = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target_test = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Declare distance metric
# L1
distance = tf.reduce_sum(tf.abs(tf.subtract(x_data_train, tf.expand_dims(x_data_test, 1))), axis=2)

# L2
#distance = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x_data_train, tf.expand_dims(x_data_test,1))), reduction_indices=1))

# Predict: Get min distance index (Nearest neighbor)
#prediction = tf.arg_min(distance, 0)
top_k_xvals, top_k_indices = tf.nn.top_k(tf.negative(distance), k=k)
x_sums = tf.expand_dims(tf.reduce_sum(top_k_xvals, 1),1)
x_sums_repeated = tf.matmul(x_sums,tf.ones([1, k], tf.float32))
x_val_weights = tf.expand_dims(tf.div(top_k_xvals,x_sums_repeated), 1)

top_k_yvals = tf.gather(y_target_train, top_k_indices)
prediction = tf.squeeze(tf.matmul(x_val_weights,top_k_yvals), axis=[1])
#prediction = tf.reduce_mean(top_k_yvals, 1)

# Calculate MSE
mse = tf.div(tf.reduce_sum(tf.square(tf.subtract(prediction, y_target_test))), batch_size)

# Calculate how many loops over training data
num_loops = int(np.ceil(len(x_vals_test)/batch_size))

for i in range(num_loops):
    min_index = i*batch_size
    max_index = min((i+1)*batch_size,len(x_vals_train))
    x_batch = x_vals_test[min_index:max_index]
    y_batch = y_vals_test[min_index:max_index]
    predictions = sess.run(prediction, feed_dict={x_data_train: x_vals_train, x_data_test: x_batch,
                                         y_target_train: y_vals_train, y_target_test: y_batch})
    batch_mse = sess.run(mse, feed_dict={x_data_train: x_vals_train, x_data_test: x_batch,
                                         y_target_train: y_vals_train, y_target_test: y_batch})

    print('Batch #' + str(i+1) + ' MSE: ' + str(np.round(batch_mse,3)))

# Plot prediction and actual distribution
bins = np.linspace(5, 50, 45)

plt.hist(predictions, bins, alpha=0.5, label='Prediction')
plt.hist(y_batch, bins, alpha=0.5, label='Actual')
plt.title('Histogram of Predicted and Actual Values')
plt.xlabel('Med Home Value in $1,000s')
plt.ylabel('Frequency')
plt.legend(loc='upper right')
plt.show()