import pandas as pd

import graphlab

# pass in column names for each CSV and read them using pandas.

# Reading users file:

u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']

users = pd.read_csv('ml-100k/u.user', sep='|', names=u_cols,

encoding='latin-1')

# Reading ratings file:

r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']

ratings = pd.read_csv('ml-100k/u.data', sep='\t', names=r_cols,

encoding='latin-1')

# Reading items file:

i_cols = ['movie id', 'movie title', 'release date', 'video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure',

'Animation', 'Children\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',

'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']

items = pd.read_csv('ml-100k/u.item', sep='|', names=i_cols,

encoding='latin-1')

# content of each file to understand them better.

print ("Users "),

print users.shape

users.head()

print("\n Ratings"),

print ratings.shape

ratings.head()

print("\n items"),

print items.shape

items.head()

# Now divide the ratings data set into test and train data for making models.

# Luckily GroupLens provides pre-divided data wherein the test data has 10 ratings for each user,

# i.e. 9430 rows in total. Lets load that:

r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']

ratings_base = pd.read_csv('ml-100k/ua.base', sep='\t', names=r_cols, encoding='latin-1')

ratings_test = pd.read_csv('ml-100k/ua.test', sep='\t', names=r_cols, encoding='latin-1')

#ratings_base.shape

#ratings_test.shape

# lets convert these in SFrames.

train_data = graphlab.SFrame(ratings_base)

test_data = graphlab.SFrame(ratings_test)

# A Simple Popularity Model

popularity_model = graphlab.popularity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating')

#Lets use this model to make top 5 recommendations for first 5 users and see what comes out

#Get recommendations for first 5 users and print them

#users = range(1,6) specifies user ID of first 5 users

#k=5 specifies top 5 recommendations to be given

print("Popularity Model")

popularity_recomm = popularity_model.recommend(users=range(1,6),k=5)

popularity_recomm.print_rows(num_rows=25)

#A Collaborative Filtering Model

#Lets create a model based on item similarity as follow:

#Train Model

item_sim_pearson = graphlab.item_similarity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating', similarity_type='pearson')

item_sim_cosine= graphlab.item_similarity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating', similarity_type='cosine')

item_sim_jaccard = graphlab.item_similarity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating', similarity_type='jaccard')

#Make Recommendations:

print("\n Collaborative Filtering Model(pearson)")

item_sim_recomm = item_sim_pearson.recommend(users=range(1,6),k=5)

item_sim_recomm.print_rows(num_rows=25)

print("\n Collaborative Filtering Model(cosine)")

item_sim_recomm1 = item_sim_cosine.recommend(users=range(1,6),k=5)

item_sim_recomm1.print_rows(num_rows=25)

print("\n Collaborative Filtering Model(jaccard)")

item_sim_recomm2 = item_sim_jaccard.recommend(users=range(1,6),k=5)

item_sim_recomm2.print_rows(num_rows=25)

#graphlab.item_similarity_recommender.compare_models(train_data, [popularity_model, item_sim_model,item_sim_model1,item_sim_model2],metric='precision_recall')

#Evaluating Recommendation Engines

#Lets compare both the models built till now based on precision-recall characteristics:

model_performance = graphlab.compare(test_data, [popularity_model, item_sim_pearson,item_sim_cosine,item_sim_jaccard])

graphlab.show_comparison(model_performance,[popularity_model, item_sim_pearson,item_sim_cosine,item_sim_jaccard])

# factorization method

#fact_rec_model=graphlab.recommender.factorization_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating', user_data=None, item_data=None, num_factors=8, regularization=1e-08, linear_regularization=1e-10, side_data_factorization=True, nmf=False, binary_target=False, max_iterations=50, sgd_step_size=0, random_seed=0, solver='auto', verbose=True)

#Make Recommendations:

#fact_sim_recomm = fact_rec_model.recommend(users=range(1,6),k=5)

#fact_sim_recomm.print_rows(num_rows=25)

graphlab.canvas.show()

graphlab.canvas.set_target('ipynb')