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collab_global.py
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collab_global.py
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import pandas as pd
import numpy as np
import math
import time
import random
precision_k = 25
num_of_users = 6040 + 1
num_of_movies= 3952 + 1
num_of_ratings = 1000209
def preprocess() :
'''
preprocessing the data by loading data into user_movie_matrix
returns : matrix_without_test_data,user_movie_matrix,global_av
'''
#Reading ratings file:
r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']
ratings = pd.read_csv('ml-1m/ratings.dat', sep="::", names=r_cols,encoding='latin-1',engine='python')
ratings= ratings.to_numpy()
indices = list(range(ratings.shape[0]))
random.shuffle(indices)
ratings = ratings[indices]
ratings= pd.DataFrame(ratings)
ratings = ratings.rename(columns={0: 'user_id',1 : 'movie_id',2 : 'rating', 3: 'unix_timestamp'},inplace= False)
#removing the timestamp
ratings = ratings[['user_id', 'movie_id', 'rating']]
#converting to list
ratings_list = ratings.values.tolist()
global_sum = 0.0
n = len(ratings_list)
#computing the global average
for i in range(0, n):
global_sum = global_sum + ratings_list[i][2]
global_av = global_sum / n
user_movie_matrix = np.zeros((num_of_users,num_of_movies))
#making the utility matrix
for i in range(num_of_ratings):
user_id = ratings_list[i][0]
movie_id = ratings_list[i][1]
rating = ratings_list[i][2]
user_movie_matrix[user_id][movie_id] = rating
mean = 0.0
#taking the first 100 * 100 matrix as test data set
matrix_without_test_data = np.copy(user_movie_matrix)
for i in range(1,101):
for j in range(1,101):
matrix_without_test_data[i][j] = 0.0
return matrix_without_test_data,user_movie_matrix,global_av
def center(matrix_without_test_data):
'''
centering the matrix around mean
parameters : matrix_without_test_data
returns : matrix_without_test_data
'''
#centering the training data set
for i in range(1,num_of_users):
sum = 0.0
count = 0.0
#computing mean of each row
for j in range(1,num_of_movies):
if(matrix_without_test_data[i][j] != 0):
sum = sum + matrix_without_test_data[i][j]
count = count + 1.0
mean = sum / count
#centering the values of each row
for j in range(1,num_of_movies):
if(matrix_without_test_data[i][j] != 0):
matrix_without_test_data[i][j] = matrix_without_test_data[i][j] - mean
else:
matrix_without_test_data[i][j] = mean
return matrix_without_test_data
def main(matrix_without_test_data,user_movie_matrix,global_av):
'''
Predicting values and calculating errors
parameters : matrix_without_test_data,user_movie_matrix,global_av
Finally prints RMSE , top k precision ,Spearman
'''
similarity = 0.0
predict = 0.0
count = 0.0
squares_sum = 0.0
count_sq = 0.0
start = time.time()
precision_rating = []
for a in range(1,21):
for b in range(1,21):
if(user_movie_matrix[a][b] != 0):
user_sum = 0.0
#compute the bias of the user
for i in range(1,num_of_movies):
if(matrix_without_test_data[a][i] != 0):
user_sum = user_sum + matrix_without_test_data[a][i]
count = count + 1.0
user_av = user_sum / count
user_dev = user_av - global_av
movie_sum = 0.0
count = 0.0
#compute the bias of the movie
for i in range(1,num_of_users):
if(matrix_without_test_data[i][b] != 0):
movie_sum = movie_sum + matrix_without_test_data[i][b]
count = count + 1.0
movie_av = movie_sum / count
movie_dev = movie_av - global_av
count = 0.0
#overall bias
bxi = global_av + user_dev + movie_dev
#to compute the dot products with each other user
if(user_movie_matrix[a][b] != 0):
col_A = matrix_without_test_data[:,b]
dot_products = np.zeros((num_of_movies,1))
for k in range(1, num_of_movies):
if(matrix_without_test_data[a][k] != 0):
col_B = matrix_without_test_data[:,k]
A = np.sqrt(np.sum(col_A**2))
B = np.sqrt(np.sum(col_B**2))
if(A == 0 or B == 0):
similarity = 0.0
else:
#computing the cosine similarity
similarity = (np.sum(np.multiply(col_A,col_B))) / (np.sqrt(np.sum(col_A**2)) * np.sqrt(np.sum(col_B**2)))
dot_products[k][0] = similarity
predict = 0.0
count = 0.0
countj = 0.0
moviej_sum = 0.0
#computing the bias in the movies
for k in range(1,num_of_movies):
if(matrix_without_test_data[a][k] != 0 and dot_products[k][0] > 0):
for i in range(1,num_of_users):
if(matrix_without_test_data[i][k] != 0):
moviej_sum = moviej_sum + matrix_without_test_data[i][k]
countj = countj + 1.0
if(countj == 0):
continue
moviej_av = moviej_sum / countj
moviej_dev = moviej_av - global_av
bxj = global_av + user_dev + moviej_dev
#computing the prediction with the bias
predict = predict + dot_products[k][0] * (matrix_without_test_data[i][k] - bxj)
count = count + dot_products[k][0]
if(count > 0):
temp = predict
predict = predict / count
predict = predict + bxi
precision_rating.append(predict)
print("Predicted Rating ")
print(predict)
print("Actual Rating ")
print(user_movie_matrix[a][b])
#computing rmse
squares_sum = squares_sum + (predict - user_movie_matrix[a][b])**2
count_sq = count_sq + 1.0
if(count_sq > 1):
correlation = 1 - ((6 * squares_sum) / (count_sq**3 - count_sq))
print(count_sq)
print("Root mean squared error")
print(math.sqrt(squares_sum / count_sq))
print("Spearman's correlation")
print(correlation)
#computing the precision at top k
precision_rating.sort(reverse=True)
countk = 0.0
for i in range(0,precision_k):
if(precision_rating[i] >= 3):
countk = countk + 1
precision_at_topk = countk / precision_k
print("Precision at top k")
print(precision_at_topk)
print("Time required for collaborative filtering with global baseline ")
print("--- %s seconds ---" % (time.time() - start))
if __name__ == "__main__":
matrix_without_test_data,user_movie_matrix,global_av = preprocess()
matrix_without_test_data = center(matrix_without_test_data)
main(matrix_without_test_data,user_movie_matrix,global_av)