# Combining Everything Together

#----------------------------------

# This file will perform binary classification on the

# iris dataset. We will only predict if a flower is

# I.setosa or not.

#

# We will create a simple binary classifier by creating a line

# and running everything through a sigmoid to get a binary predictor.

# The two features we will use are pedal length and pedal width.

#

# We will use batch training, but this can be easily

# adapted to stochastic training.

import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets

import tensorflow as tf

from tensorflow.python.framework import ops

ops.reset_default_graph()

# Load the iris data

# iris.target = {0, 1, 2}, where '0' is setosa

# iris.data ~ [sepal.width, sepal.length, pedal.width, pedal.length]

iris = datasets.load_iris()

binary_target = np.array([1. if x==0 else 0. for x in iris.target])

iris_2d = np.array([[x[2], x[3]] for x in iris.data])

# Declare batch size

batch_size = 20

# Create graph

sess = tf.Session()

# Declare placeholders

x1_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)

x2_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)

y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Create variables A and b (0 = x1 - A*x2 + b)

A = tf.Variable(tf.random_normal(shape=[1, 1]))

b = tf.Variable(tf.random_normal(shape=[1, 1]))

# Add model to graph:

# x1 - A*x2 + b

my_mult = tf.matmul(x2_data, A)

my_add = tf.add(my_mult, b)

my_output = tf.subtract(x1_data, my_add)

# Add classification loss (cross entropy)

xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)

# Create Optimizer

my_opt = tf.train.GradientDescentOptimizer(0.05)

train_step = my_opt.minimize(xentropy)

# Initialize variables

init = tf.global_variables_initializer()

sess.run(init)

# Run Loop

for i in range(1000):

    rand_index = np.random.choice(len(iris_2d), size=batch_size)

    #rand_x = np.transpose([iris_2d[rand_index]])

    rand_x = iris_2d[rand_index]

    rand_x1 = np.array([[x[0]] for x in rand_x])

    rand_x2 = np.array([[x[1]] for x in rand_x])

    #rand_y = np.transpose([binary_target[rand_index]])

    rand_y = np.array([[y] for y in binary_target[rand_index]])

    sess.run(train_step, feed_dict={x1_data: rand_x1, x2_data: rand_x2, y_target: rand_y})

    if (i+1)%200==0:

        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ', b = ' + str(sess.run(b)))

# Visualize Results

# Pull out slope/intercept

[[slope]] = sess.run(A)

[[intercept]] = sess.run(b)

# Create fitted line

x = np.linspace(0, 3, num=50)

ablineValues = []

for i in x:

  ablineValues.append(slope*i+intercept)

# Plot the fitted line over the data

setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==1]

setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==1]

non_setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==0]

non_setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==0]

plt.plot(setosa_x, setosa_y, 'rx', ms=10, mew=2, label='setosa')

plt.plot(non_setosa_x, non_setosa_y, 'ro', label='Non-setosa')

plt.plot(x, ablineValues, 'b-')

plt.xlim([0.0, 2.7])

plt.ylim([0.0, 7.1])

plt.suptitle('Linear Separator For I.setosa', fontsize=20)

plt.xlabel('Petal Length')

plt.ylabel('Petal Width')

plt.legend(loc='lower right')

plt.show()