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matrix_factorisation_svd.py
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matrix_factorisation_svd.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import os
data_path = 'ml-20m/'
movies_filename = 'movies.csv'
ratings_filename = 'ratings.csv'
df_movies = pd.read_csv(
os.path.join(data_path, movies_filename),
# movies_filename,
usecols=['movieId', 'title'],
dtype={'movieId': 'int32', 'title': 'str'})
df_ratings = pd.read_csv(
os.path.join(data_path, ratings_filename),
# ratings_filename,
usecols=['userId', 'movieId', 'rating'],
dtype={'userId': 'int32', 'movieId': 'int32', 'rating': 'float32'})
# In[2]:
# ratings = np.array(ratings_list)
# # users = np.array(users_list)
# movies = np.array(movies_list)
# In[3]:
df_movies.head()
# In[4]:
df_ratings.head()
# In[5]:
df_ratings=df_ratings[:2000000]
df_movie_features = df_ratings.pivot(
index='userId',
columns='movieId',
values='rating'
).fillna(0)
# In[6]:
df_movie_features.head()
# In[7]:
R = df_movie_features.as_matrix()
user_ratings_mean = np.mean(R, axis = 1)
R_demeaned = R - user_ratings_mean.reshape(-1, 1)
# In[8]:
from scipy.sparse.linalg import svds
U, sigma, Vt = svds(R_demeaned, k = 50)
# In[9]:
# Done.
# that the Sigma$ returned is just the values instead of a diagonal matrix.
# This is useful, but since I'm going to leverage matrix multiplication to get predictions
# I'll convert it to the diagonal matrix form.
sigma = np.diag(sigma)
# In[10]:
all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) + user_ratings_mean.reshape(-1, 1)
# In[11]:
preds_df = pd.DataFrame(all_user_predicted_ratings, columns = df_movie_features.columns)
preds_df.head()
# In[20]:
# def recommend_movies(predictions_df, userID, movies_df, original_ratings_df, num_recommendations=10):
# # Get and sort the user's predictions
# user_row_number = userID - 1 # UserID starts at 1, not 0
# sorted_user_predictions = preds_df.iloc[user_row_number].sort_values(ascending=False) # UserID starts at 1
# print(sorted_user_predictions)
# # Get the user's data and merge in the movie information.
# user_data = original_ratings_df[original_ratings_df.userId == (userID)]
# user_full = (user_data.merge(movies_df, how = 'left', left_on = 'movieId', right_on = 'movieId').
# sort_values(['rating'], ascending=False)
# )
# # print ('User {0} has already rated {1} movies').format(userID, user_full.shape[0])
# # print( 'Recommending highest {0} predicted ratings movies not already rated.').format(num_recommendations)
# # Recommend the highest predicted rating movies that the user hasn't seen yet.
# recommendations = (movies_df[~movies_df['movieId'].isin(user_full['movieId'])].
# merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left').rename(columns = {user_row_number: 'Predictions'}).
# sort_values('Predictions', ascending = False).
# iloc[:num_recommendations, :-1]
# )
# return user_full, recommendations
def recommend_movies(preds_df, userID, movies_df, original_ratings_df, num_recommendations=5):
# Get and sort the user's predictions
user_row_number = userID - 1 # UserID starts at 1, not 0
sorted_user_predictions = preds_df.iloc[user_row_number].sort_values(ascending=False) # UserID starts at 1
# print(preds_df.iloc[user_row_number])
# print(sorted_user_predictions)
# Get the user's data and merge in the movie information.
user_data = original_ratings_df[original_ratings_df.userId == (userID)]
user_full = (user_data.merge(movies_df, how = 'left', left_on = 'movieId', right_on = 'movieId').
sort_values(['rating'], ascending=False)
)
# print(user_full)
# print 'User {0} has already rated {1} movies.'.format(userID, user_full.shape[0])
# print 'Recommending highest {0} predicted ratings movies not already rated.'.format(num_recommendations)
# left_on = 'movieId',
# right_on = 'movieId').
# merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left').rename(columns = {user_row_number: 'Predictions'}).
# Recommend the highest predicted rating movies that the user hasn't seen yet.
recommendations = (movies_df[~movies_df['movieId'].isin(user_full['movieId'])]).merge(pd.DataFrame(sorted_user_predictions).reset_index(), how = 'left', left_on = 'movieId',
right_on = 'movieId').rename(columns = {user_row_number: 'Predictions'}).sort_values('Predictions', ascending = False).iloc[:num_recommendations, :-1]
return user_full, recommendations
# In[21]:
already_rated, predictions = recommend_movies(preds_df, 330, df_movies, df_ratings, 10)
# In[22]:
already_rated.head(10)
# In[23]:
predictions
# In[ ]: