<filename>old_files/book_reco.py
'''
We don't explicitly need to specify a gpu device.
It will be picked up on it's own if CUDA is correctly installed.
Note that the tensorflow-gpu and cuda version should be compatible.
I am using tf - 1.4.1 with cuda - 8.0. If you're are using cuda 0.9 or later,
you will need a different version of tensorflow-gpu

We are going to use Restricted Boltzmann Machines for this project. They have
only two layers in the network (one hidden and one visible). The hidden layer
has the ability to learn latent features from the input and this helps in
categorizing the input into different classes. For example, if the input is the
user preference (in binary - likes or didn't like) for 6 books and the hidden
layer has 2 units, the latent features learnt could be the categories of books -
"Crime" and "Romance". This is then used to reconstruct user input which helps
in creating a score about the user choice. This score is finally used to
recommend the books.
'''

# importing required libraries
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
plt.switch_backend('agg')

# Loading the books and ratings
#n = 500
# loading every 500th line as the dataset is too large
# and tensorflow fails to create a tensor of such a large size
#ratings = pd.read_csv("data/ratings.csv", skiprows=lambda i: i % n != 0)
ratings = pd.read_csv('data/ratings.csv')
to_read = pd.read_csv('data/to_read.csv')
books = pd.read_csv('data/books.csv')

temp = ratings.sort_values(by=['user_id'], ascending=True)
ratings = temp.iloc[:200000, :]

# creating a new column with indexes to ease the process of creating our training data
ratings = ratings.reset_index(drop=True)
ratings['List Index'] = ratings.index
readers_group = ratings.groupby("user_id")

total = []
#train = np.zeros((readers_group.shape[0], len(ratings)))
#usedReaders = 500
for readerID, curReader in readers_group:
    #temp = [0] * len(ratings)
    temp = np.zeros(len(ratings))

    for num, book in curReader.iterrows():
        temp[book['List Index']] = book['rating'] / 5.0

    total.append(temp)
    # this code chunk can be used if we want to stop after
    # a specific number of readers from our data
    '''
    if usedReaders == 0:
        break
    usedReaders -= 1
    print("readers remaining: ", usedReaders)
    '''
# Not a good idea to print train as it cannot be held into memory all at once.
# print(train)

# Dividing the total data into train and validation sets
random.shuffle(total)
print("total size of the data is: {0}".format(len(total)))

train = total[:1500]
valid = total[1500:]
print("size of the training data is: {0}".format(len(train)))
print("size of the validation data is: {0}".format(len(valid)))

print("Setting the models Parameters")
hiddenUnits = 64
visibleUnits = len(ratings)

vb = tf.placeholder(tf.float32, [visibleUnits])  # Number of unique books
# Number of features were going to learn
hb = tf.placeholder(tf.float32, [hiddenUnits])
W = tf.placeholder(tf.float32, [visibleUnits, hiddenUnits])  # Weight Matrix

print("Phase 1: Input Processing")
v0 = tf.placeholder("float", [None, visibleUnits])
_h0 = tf.nn.sigmoid(tf.matmul(v0, W) + hb)  # Visible layer activation
# Gibb's Sampling
h0 = tf.nn.relu(tf.sign(_h0 - tf.random_uniform(tf.shape(_h0))))

print("Phase 2: Reconstruction")
_v1 = tf.nn.sigmoid(tf.matmul(h0, tf.transpose(W)) +
                    vb)  # Hidden layer activation
v1 = tf.nn.relu(tf.sign(_v1 - tf.random_uniform(tf.shape(_v1))))
h1 = tf.nn.sigmoid(tf.matmul(v1, W) + hb)

print("Setting RBM Training Parameters")
# Learning rate
alpha = 0.6

print("Creating the gradients")
w_pos_grad = tf.matmul(tf.transpose(v0), h0)
w_neg_grad = tf.matmul(tf.transpose(v1), h1)

# Calculate the Contrastive Divergence to maximize
CD = (w_pos_grad - w_neg_grad) / tf.to_float(tf.shape(v0)[0])

# Create methods to update the weights and biases
update_w = W + alpha * CD
update_vb = vb + alpha * tf.reduce_mean(v0 - v1, 0)
update_hb = hb + alpha * tf.reduce_mean(h0 - h1, 0)

# Set the error function, here we use Mean Absolute Error Function
err = v0 - v1
err_sum = tf.reduce_mean(err * err)

""" Initialize our Variables with Zeroes using Numpy Library """
# Current weight
cur_w = np.random.normal(loc=0, scale=0.01, size=[visibleUnits, hiddenUnits])

# Current visible unit biases
cur_vb = np.zeros([visibleUnits], np.float32)

# Current hidden unit biases
cur_hb = np.zeros([hiddenUnits], np.float32)

# Previous weight
prv_w = np.zeros([visibleUnits, hiddenUnits], np.float32)

# Previous visible unit biases
prv_vb = np.zeros([visibleUnits], np.float32)

# Previous hidden unit biases
prv_hb = np.zeros([hiddenUnits], np.float32)

print("Running the session")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())

def free_energy(v_sample, W, vb, hb):
    ''' Function to compute the free energy '''
    wx_b = np.dot(v_sample, W) + hb
    vbias_term = np.dot(v_sample, vb)
    hidden_term = np.sum(np.log(1 + np.exp(wx_b)), axis = 1)
    return -hidden_term - vbias_term


# Training RBM with 60 Epochs, with Each Epoch using batch size of 100.
# After training print out the error with epoch number.
print("Starting the training process")
epochs = 60
batchsize = 100
errors = []
energy_train = []
energy_valid = []
for i in range(epochs):
    for start, end in zip(range(0, len(train), batchsize), range(batchsize, len(train), batchsize)):
        batch = train[start:end]
        cur_w = sess.run(update_w, feed_dict={
                         v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        cur_vb = sess.run(update_vb, feed_dict={
                          v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        cur_hb = sess.run(update_hb, feed_dict={
                          v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        prv_w = cur_w
        prv_vb = cur_vb
        prv_hb = cur_hb

    energy_train.append(np.mean(free_energy(train, cur_w, cur_vb, cur_hb)))
    #print("Epoch: {0}, free energy: {1}".format(i, energy_train[i]))
    energy_valid.append(np.mean(free_energy(valid, cur_w, cur_vb, cur_hb)))

    errors.append(sess.run(err_sum, feed_dict={
                  v0: train, W: cur_w, vb: cur_vb, hb: cur_hb}))
    if i % 10 == 0:
        print("Error in epoch {0} is: {1}".format(i, errors[i]))


# This is the input that we need to provide manually, that is the user number
user = 22
inputUser = [train[user]]

# Feeding in the User and Reconstructing the input
hh0 = tf.nn.sigmoid(tf.matmul(v0, W) + hb)
vv1 = tf.nn.sigmoid(tf.matmul(hh0, tf.transpose(W)) + vb)
feed = sess.run(hh0, feed_dict={v0: inputUser, W: prv_w, hb: prv_hb})
rec = sess.run(vv1, feed_dict={hh0: feed, W: prv_w, vb: prv_vb})

# Creating recommendation score for books in our data
ratings["Recommendation Score"] = rec[0]

""" Recommend User what books he has not read yet """
# Find the mock user's user_id from the data
cur_user_id = ratings.iloc[user]['user_id']

# Find all books the mock user has read before
read_books = ratings[ratings['user_id'] == cur_user_id]['book_id']
read_books

# converting the pandas series object into a list
read_books_id = read_books.tolist()

# getting the book names and authors for the books already read by the user
read_books_names = []
read_books_authors = []
for book in read_books_id:
    read_books_names.append(
        books[books['book_id'] == book]['original_title'].tolist()[0])
    read_books_authors.append(
        books[books['book_id'] == book]['authors'].tolist()[0])

# Find all books the mock user has 'not' read before using the to_read data
unread_books = to_read[to_read['user_id'] == cur_user_id]['book_id']
unread_books_id = unread_books.tolist()
unread_books_id

# extract the ratings of all the unread books from ratings dataframe
unread_with_score = ratings[ratings['book_id'].isin(unread_books_id)]
unread_with_score

# grouping the unread data on book id and taking the mean of the recommendation scores for each book_id
grouped_unread = unread_with_score.groupby('book_id', as_index=False)[
    'Recommendation Score'].mean()
grouped_unread

# getting the names and authors of the unread books
unread_books_names = []
unread_books_authors = []
unread_books_scores = []
for book in grouped_unread['book_id']:
    unread_books_names.append(
        books[books['book_id'] == book]['original_title'].tolist()[0])
    unread_books_authors.append(
        books[books['book_id'] == book]['authors'].tolist()[0])
    unread_books_scores.append(
        grouped_unread[grouped_unread['book_id'] == book]['Recommendation Score'].tolist()[0])

# creating a data frame for unread books with their names, authors and recommendation scores
unread_books_with_scores = pd.DataFrame({
    'book_name': unread_books_names,
    'book_authors': unread_books_authors,
    'score': unread_books_scores
})

# creating a data frame for read books with the names and authors
read_books_with_names = pd.DataFrame({
    'book_name': read_books_names,
    'book_authors': read_books_authors
})

# sort the result in descending order of the recommendation score
sorted_result = unread_books_with_scores.sort_values(
    by='score', ascending=False)

# exporting the read and unread books  with scores to csv files
read_books_with_names.to_csv('results/read_books_with_names.csv')
sorted_result.to_csv('results/unread_books_with_scores.csv')
print('The books read by the user are:')
print(read_books_with_names)
print('The books recommended to the user are:')
print(sorted_result)

# Saving the plots
plt.plot(errors)
plt.xlabel("Epoch")
plt.ylabel("Error")
plt.savefig("error.png")

fig, ax = plt.subplots()
ax.plot(energy_train, label='train')
ax.plot(energy_valid, label='valid')
leg = ax.legend()
plt.xlabel("Epoch")
plt.ylabel("Free Energy")
plt.savefig("free_energy.png")

<filename>old_files/book_reco.py
'''
We don't explicitly need to specify a gpu device.
It will be picked up on it's own if CUDA is correctly installed.
Note that the tensorflow-gpu and cuda version should be compatible.
I am using tf - 1.4.1 with cuda - 8.0. If you're are using cuda 0.9 or later,
you will need a different version of tensorflow-gpu

We are going to use Restricted Boltzmann Machines for this project. They have
only two layers in the network (one hidden and one visible). The hidden layer
has the ability to learn latent features from the input and this helps in
categorizing the input into different classes. For example, if the input is the
user preference (in binary - likes or didn't like) for 6 books and the hidden
layer has 2 units, the latent features learnt could be the categories of books -
"Crime" and "Romance". This is then used to reconstruct user input which helps
in creating a score about the user choice. This score is finally used to
recommend the books.
'''

# importing required libraries
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
plt.switch_backend('agg')

# Loading the books and ratings
#n = 500
# loading every 500th line as the dataset is too large
# and tensorflow fails to create a tensor of such a large size
#ratings = pd.read_csv("data/ratings.csv", skiprows=lambda i: i % n != 0)
ratings = pd.read_csv('data/ratings.csv')
to_read = pd.read_csv('data/to_read.csv')
books = pd.read_csv('data/books.csv')

temp = ratings.sort_values(by=['user_id'], ascending=True)
ratings = temp.iloc[:200000, :]

# creating a new column with indexes to ease the process of creating our training data
ratings = ratings.reset_index(drop=True)
ratings['List Index'] = ratings.index
readers_group = ratings.groupby("user_id")

total = []
#train = np.zeros((readers_group.shape[0], len(ratings)))
#usedReaders = 500
for readerID, curReader in readers_group:
    #temp = [0] * len(ratings)
    temp = np.zeros(len(ratings))

    for num, book in curReader.iterrows():
        temp[book['List Index']] = book['rating'] / 5.0

    total.append(temp)
    # this code chunk can be used if we want to stop after
    # a specific number of readers from our data
    '''
    if usedReaders == 0:
        break
    usedReaders -= 1
    print("readers remaining: ", usedReaders)
    '''
# Not a good idea to print train as it cannot be held into memory all at once.
# print(train)

# Dividing the total data into train and validation sets
random.shuffle(total)
print("total size of the data is: {0}".format(len(total)))

train = total[:1500]
valid = total[1500:]
print("size of the training data is: {0}".format(len(train)))
print("size of the validation data is: {0}".format(len(valid)))

print("Setting the models Parameters")
hiddenUnits = 64
visibleUnits = len(ratings)

vb = tf.placeholder(tf.float32, [visibleUnits])  # Number of unique books
# Number of features were going to learn
hb = tf.placeholder(tf.float32, [hiddenUnits])
W = tf.placeholder(tf.float32, [visibleUnits, hiddenUnits])  # Weight Matrix

print("Phase 1: Input Processing")
v0 = tf.placeholder("float", [None, visibleUnits])
_h0 = tf.nn.sigmoid(tf.matmul(v0, W) + hb)  # Visible layer activation
# Gibb's Sampling
h0 = tf.nn.relu(tf.sign(_h0 - tf.random_uniform(tf.shape(_h0))))

print("Phase 2: Reconstruction")
_v1 = tf.nn.sigmoid(tf.matmul(h0, tf.transpose(W)) +
                    vb)  # Hidden layer activation
v1 = tf.nn.relu(tf.sign(_v1 - tf.random_uniform(tf.shape(_v1))))
h1 = tf.nn.sigmoid(tf.matmul(v1, W) + hb)

print("Setting RBM Training Parameters")
# Learning rate
alpha = 0.6

print("Creating the gradients")
w_pos_grad = tf.matmul(tf.transpose(v0), h0)
w_neg_grad = tf.matmul(tf.transpose(v1), h1)

# Calculate the Contrastive Divergence to maximize
CD = (w_pos_grad - w_neg_grad) / tf.to_float(tf.shape(v0)[0])

# Create methods to update the weights and biases
update_w = W + alpha * CD
update_vb = vb + alpha * tf.reduce_mean(v0 - v1, 0)
update_hb = hb + alpha * tf.reduce_mean(h0 - h1, 0)

# Set the error function, here we use Mean Absolute Error Function
err = v0 - v1
err_sum = tf.reduce_mean(err * err)

""" Initialize our Variables with Zeroes using Numpy Library """
# Current weight
cur_w = np.random.normal(loc=0, scale=0.01, size=[visibleUnits, hiddenUnits])

# Current visible unit biases
cur_vb = np.zeros([visibleUnits], np.float32)

# Current hidden unit biases
cur_hb = np.zeros([hiddenUnits], np.float32)

# Previous weight
prv_w = np.zeros([visibleUnits, hiddenUnits], np.float32)

# Previous visible unit biases
prv_vb = np.zeros([visibleUnits], np.float32)

# Previous hidden unit biases
prv_hb = np.zeros([hiddenUnits], np.float32)

print("Running the session")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())

def free_energy(v_sample, W, vb, hb):
    ''' Function to compute the free energy '''
    wx_b = np.dot(v_sample, W) + hb
    vbias_term = np.dot(v_sample, vb)
    hidden_term = np.sum(np.log(1 + np.exp(wx_b)), axis = 1)
    return -hidden_term - vbias_term


# Training RBM with 60 Epochs, with Each Epoch using batch size of 100.
# After training print out the error with epoch number.
print("Starting the training process")
epochs = 60
batchsize = 100
errors = []
energy_train = []
energy_valid = []
for i in range(epochs):
    for start, end in zip(range(0, len(train), batchsize), range(batchsize, len(train), batchsize)):
        batch = train[start:end]
        cur_w = sess.run(update_w, feed_dict={
                         v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        cur_vb = sess.run(update_vb, feed_dict={
                          v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        cur_hb = sess.run(update_hb, feed_dict={
                          v0: batch, W: prv_w, vb: prv_vb, hb: prv_hb})
        prv_w = cur_w
        prv_vb = cur_vb
        prv_hb = cur_hb

    energy_train.append(np.mean(free_energy(train, cur_w, cur_vb, cur_hb)))
    #print("Epoch: {0}, free energy: {1}".format(i, energy_train[i]))
    energy_valid.append(np.mean(free_energy(valid, cur_w, cur_vb, cur_hb)))

    errors.append(sess.run(err_sum, feed_dict={
                  v0: train, W: cur_w, vb: cur_vb, hb: cur_hb}))
    if i % 10 == 0:
        print("Error in epoch {0} is: {1}".format(i, errors[i]))


# This is the input that we need to provide manually, that is the user number
user = 22
inputUser = [train[user]]

# Feeding in the User and Reconstructing the input
hh0 = tf.nn.sigmoid(tf.matmul(v0, W) + hb)
vv1 = tf.nn.sigmoid(tf.matmul(hh0, tf.transpose(W)) + vb)
feed = sess.run(hh0, feed_dict={v0: inputUser, W: prv_w, hb: prv_hb})
rec = sess.run(vv1, feed_dict={hh0: feed, W: prv_w, vb: prv_vb})

# Creating recommendation score for books in our data
ratings["Recommendation Score"] = rec[0]

""" Recommend User what books he has not read yet """
# Find the mock user's user_id from the data
cur_user_id = ratings.iloc[user]['user_id']

# Find all books the mock user has read before
read_books = ratings[ratings['user_id'] == cur_user_id]['book_id']
read_books

# converting the pandas series object into a list
read_books_id = read_books.tolist()

# getting the book names and authors for the books already read by the user
read_books_names = []
read_books_authors = []
for book in read_books_id:
    read_books_names.append(
        books[books['book_id'] == book]['original_title'].tolist()[0])
    read_books_authors.append(
        books[books['book_id'] == book]['authors'].tolist()[0])

# Find all books the mock user has 'not' read before using the to_read data
unread_books = to_read[to_read['user_id'] == cur_user_id]['book_id']
unread_books_id = unread_books.tolist()
unread_books_id

# extract the ratings of all the unread books from ratings dataframe
unread_with_score = ratings[ratings['book_id'].isin(unread_books_id)]
unread_with_score

# grouping the unread data on book id and taking the mean of the recommendation scores for each book_id
grouped_unread = unread_with_score.groupby('book_id', as_index=False)[
    'Recommendation Score'].mean()
grouped_unread

# getting the names and authors of the unread books
unread_books_names = []
unread_books_authors = []
unread_books_scores = []
for book in grouped_unread['book_id']:
    unread_books_names.append(
        books[books['book_id'] == book]['original_title'].tolist()[0])
    unread_books_authors.append(
        books[books['book_id'] == book]['authors'].tolist()[0])
    unread_books_scores.append(
        grouped_unread[grouped_unread['book_id'] == book]['Recommendation Score'].tolist()[0])

# creating a data frame for unread books with their names, authors and recommendation scores
unread_books_with_scores = pd.DataFrame({
    'book_name': unread_books_names,
    'book_authors': unread_books_authors,
    'score': unread_books_scores
})

# creating a data frame for read books with the names and authors
read_books_with_names = pd.DataFrame({
    'book_name': read_books_names,
    'book_authors': read_books_authors
})

# sort the result in descending order of the recommendation score
sorted_result = unread_books_with_scores.sort_values(
    by='score', ascending=False)

# exporting the read and unread books  with scores to csv files
read_books_with_names.to_csv('results/read_books_with_names.csv')
sorted_result.to_csv('results/unread_books_with_scores.csv')
print('The books read by the user are:')
print(read_books_with_names)
print('The books recommended to the user are:')
print(sorted_result)

# Saving the plots
plt.plot(errors)
plt.xlabel("Epoch")
plt.ylabel("Error")
plt.savefig("error.png")

fig, ax = plt.subplots()
ax.plot(energy_train, label='train')
ax.plot(energy_valid, label='valid')
leg = ax.legend()
plt.xlabel("Epoch")
plt.ylabel("Free Energy")
plt.savefig("free_energy.png")

We don't explicitly need to specify a gpu device. It will be picked up on it's own if CUDA is correctly installed. Note that the tensorflow-gpu and cuda version should be compatible. I am using tf - 1.4.1 with cuda - 8.0. If you're are using cuda 0.9 or later, you will need a different version of tensorflow-gpu  We are going to use Restricted Boltzmann Machines for this project. They have only two layers in the network (one hidden and one visible). The hidden layer has the ability to learn latent features from the input and this helps in categorizing the input into different classes. For example, if the input is the user preference (in binary - likes or didn't like) for 6 books and the hidden layer has 2 units, the latent features learnt could be the categories of books - "Crime" and "Romance". This is then used to reconstruct user input which helps in creating a score about the user choice. This score is finally used to recommend the books. # importing required libraries # Loading the books and ratings #n = 500 # loading every 500th line as the dataset is too large # and tensorflow fails to create a tensor of such a large size #ratings = pd.read_csv("data/ratings.csv", skiprows=lambda i: i % n != 0) # creating a new column with indexes to ease the process of creating our training data #train = np.zeros((readers_group.shape[0], len(ratings))) #usedReaders = 500 #temp = [0] * len(ratings) # this code chunk can be used if we want to stop after # a specific number of readers from our data if usedReaders == 0:         break     usedReaders -= 1     print("readers remaining: ", usedReaders) # Not a good idea to print train as it cannot be held into memory all at once. # print(train) # Dividing the total data into train and validation sets # Number of unique books # Number of features were going to learn # Weight Matrix # Visible layer activation # Gibb's Sampling # Hidden layer activation # Learning rate # Calculate the Contrastive Divergence to maximize # Create methods to update the weights and biases # Set the error function, here we use Mean Absolute Error Function Initialize our Variables with Zeroes using Numpy Library # Current weight # Current visible unit biases # Current hidden unit biases # Previous weight # Previous visible unit biases # Previous hidden unit biases Function to compute the free energy # Training RBM with 60 Epochs, with Each Epoch using batch size of 100. # After training print out the error with epoch number. #print("Epoch: {0}, free energy: {1}".format(i, energy_train[i])) # This is the input that we need to provide manually, that is the user number # Feeding in the User and Reconstructing the input # Creating recommendation score for books in our data Recommend User what books he has not read yet # Find the mock user's user_id from the data # Find all books the mock user has read before # converting the pandas series object into a list # getting the book names and authors for the books already read by the user # Find all books the mock user has 'not' read before using the to_read data # extract the ratings of all the unread books from ratings dataframe # grouping the unread data on book id and taking the mean of the recommendation scores for each book_id # getting the names and authors of the unread books # creating a data frame for unread books with their names, authors and recommendation scores # creating a data frame for read books with the names and authors # sort the result in descending order of the recommendation score # exporting the read and unread books  with scores to csv files # Saving the plots