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usingFinalPearson.py
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usingFinalPearson.py
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import pandas as pd
import numpy as np
from scipy.sparse.linalg import svds
import matplotlib.pyplot as plt
import argparse
import sys
class Books:
def __init__(self):
self.books = pd.read_csv('./Book/Books.csv')
self.users = pd.read_csv('./Book/Users.csv')
self.ratings = pd.read_csv('./Book/Ratings.csv')
# Splitting Explicit and Implicit user ratings
# we are removing the rating set which is having the rating as 0
self.ratings_explicit = self.ratings[self.ratings.bookRating != 0]
self.ratings_implicit = self.ratings[self.ratings.bookRating == 0]
# Each Books Mean ratings and Total Rating Count
self.average_rating = pd.DataFrame(
self.ratings_explicit.groupby('ISBN')['bookRating'].mean())
self.average_rating['ratingCount'] = pd.DataFrame(
self.ratings_explicit.groupby('ISBN')['bookRating'].count())
self.average_rating = self.average_rating.rename(
columns={'bookRating': 'MeanRating'})
# To get a stronger similarities
counts1 = self.ratings_explicit['userID'].value_counts()
self.ratings_explicit = self.ratings_explicit[
self.ratings_explicit['userID'].isin(counts1[counts1 >= 50].index)]
# Explicit Books and ISBN
self.explicit_ISBN = self.ratings_explicit.ISBN.unique()
self.explicit_books = self.books.loc[self.books['ISBN'].isin(
self.explicit_ISBN)]
# Look up dict for Book and BookID
self.Book_lookup = dict(
zip(self.explicit_books["ISBN"], self.explicit_books["bookTitle"]))
self.ID_lookup = dict(
zip(self.explicit_books["bookTitle"], self.explicit_books["ISBN"]))
def diagram(self):
# # rating distribution using histogram
plt.rc("font", size=15)
self.ratings.bookRating.value_counts(sort=False).plot(kind='bar')
plt.title('Rating Distribution\n')
plt.xlabel('Rating')
plt.ylabel('Count')
plt.savefig('system1.png', bbox_inches='tight')
plt.show()
# student age distribution using histogram
self.users.Age.hist(bins=[18, 20, 22, 24, 26, 28, 30, 32, 40])
plt.title('Age Distribution\n')
plt.xlabel('Age')
plt.ylabel('Count')
plt.savefig('system2.png', bbox_inches='tight')
plt.show()
def Top_Books(self, n=10, RatingCount=100, MeanRating=3):
# here we are specifying the latency of meanRating with value of 3
# and latency of RatingCount with value of 100
# this makes a threshold value for predicting the best possible book sets for the user
# books with the highest rating
# this function will not recommend any books just shows the highest rated books rated by every user
BOOKS = self.books.merge(self.average_rating, how='right', on='ISBN')
# print(Books)
M_Rating = BOOKS.loc[BOOKS.ratingCount >= RatingCount].sort_values(
'MeanRating', ascending=False).head(n)
H_Rating = BOOKS.loc[BOOKS.MeanRating >= MeanRating].sort_values(
'ratingCount', ascending=False).head(n)
# print(M_Rating)
# print(H_Rating)
return M_Rating, H_Rating
class SVD(Books):
def __init__(self, n_latent_factor=50):
super().__init__()
self.n_latent_factor = n_latent_factor
self.ratings_mat = self.ratings_explicit.pivot(
index="userID", columns="ISBN", values="bookRating").fillna(0)
self.uti_mat = self.ratings_mat.values
# normalize by each users mean
self.user_ratings_mean = np.mean(self.uti_mat, axis=1)
self.mat = self.uti_mat - self.user_ratings_mean.reshape(-1, 1)
self.explicit_users = np.sort(self.ratings_explicit.userID.unique())
self.User_lookup = dict(
zip(range(1, len(self.explicit_users)), self.explicit_users))
self.predictions = None
def scipy_SVD(self):
# singular value decomposition
U, S, Vt = svds(self.mat, k=self.n_latent_factor)
S_diag_matrix = np.diag(S)
# Reconstructing Original Prediction Matrix
X_pred = np.dot(np.dot(U, S_diag_matrix), Vt) + \
self.user_ratings_mean.reshape(-1, 1)
self.predictions = pd.DataFrame(
X_pred, columns=self.ratings_mat.columns, index=self.ratings_mat.index)
return
def Recommend_Books(self, userID, num_recommendations=10):
# Get and sort the user's predictions
# User ID starts at 1, not 0
user_row_number = self.User_lookup[userID]
sorted_user_predictions = self.predictions.loc[user_row_number].sort_values(
ascending=False)
# Get the user's data and merge in the books information.
user_data = self.ratings_explicit[self.ratings_explicit.userID == (
self.User_lookup[userID])]
user_full = (user_data.merge(self.books, how='left', left_on='ISBN', right_on='ISBN').
sort_values(['bookRating'], ascending=False)
)
# Recommend the highest predicted rating books that the user hasn't seen yet.
recom = (self.books[~self.books['ISBN'].isin(user_full['ISBN'])].
merge(pd.DataFrame(sorted_user_predictions).reset_index(), how='left',
left_on='ISBN',
right_on='ISBN'))
recom = recom.rename(columns={user_row_number: 'Predictions'})
recommend = recom.sort_values(by=['Predictions'], ascending=False)
recommendations = recommend.iloc[:num_recommendations, :-1]
return user_full, recommendations
def YN():
reply = str(input('\n\nContinue (y/n):\t')).lower().strip()
if reply[0] == 'y':
return True
if reply[0] == 'n':
return False
else:
return False
def MainCall():
cont = True
while cont:
print("\n1-Top Books\n2-Recommendation based on user id\n3-Exit\n")
choice = int(input("Enter choice :- "))
# ---------------------------------------------------------------------------------------------------------------------------------------
if choice == 1:
print("\n\ Item based collaborative filtering\n")
Top_B = Books()
Books().diagram()
High_Mean_Rating, High_Rating_Count = Top_B.Top_Books()
pd.set_option('display.max_colwidth', -1)
print("\n\nBooks having highest ratings :\n")
print(
High_Mean_Rating[['bookTitle', 'MeanRating', 'ratingCount', 'bookAuthor']])
print("\n\nBooks having highest rating count :\n")
print(High_Rating_Count[['bookTitle',
'MeanRating', 'ratingCount', 'bookAuthor']])
print("\nFor getting recommendation based on Knn pass --KNN as argument ")
# --------------------------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------
if choice == 2:
userCollaborativFiltering = SVD()
userCollaborativFiltering.scipy_SVD()
while cont:
try:
User_ID = int(
input('Enter User ID in the range {0}-{1}: '.format(1, len(userCollaborativFiltering.explicit_users))))
except ValueError:
print('Enter a number ')
sys.exit()
if User_ID in range(1, len(userCollaborativFiltering.explicit_users)):
pass
else:
print(
"Choose between {0}-{1}".format(1, len(userCollaborativFiltering.explicit_users)))
sys.exit()
Rated_Books, SVD_Recommended_Books = userCollaborativFiltering.Recommend_Books(
userID=User_ID)
pd.set_option('display.max_colwidth', -1)
#
# print("\nThe Books already rated by the user\n")
# print(Rated_Books[['bookTitle', 'bookRating']])
print("\nRecommended Books for the user\n")
SVD_Recommended_Books = SVD_Recommended_Books.merge(
userCollaborativFiltering.average_rating, how='left', on='ISBN')
SVD_Recommended_Books = SVD_Recommended_Books.rename(
columns={'bookRating': 'MeanRating'})
print(SVD_Recommended_Books[[
'bookTitle', 'MeanRating', 'bookAuthor']])
cont = YN()
# ----------------------------------------------------------------------------------------------------------------------
if choice == 3:
sys.exit()
# ------------------------------------------------------------------------------------------------------------------
# for choice search
cont = YN()
MainCall()