In [1]:

    

import os
os.chdir('..')


    

In [2]:

    

# Import all the packages we need to generate recommendations
import numpy as np
import pandas as pd
import src.utils as utils
import src.recommenders as recommenders
import src.similarity as similarity

# imports necesary for plotting
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline  

# Enable logging on Jupyter notebook
import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)


    

In [3]:

    

# loads dataset 
dataset_folder = os.path.join(os.getcwd(), 'data')
dataset_folder_ready = utils.load_dataset(dataset_folder)

# adds personal ratings to original dataset ratings file.
ratings_file = os.path.join(dataset_folder, 'ml-latest-small','ratings-merged.csv')
[ratings, my_customer_number] = utils.merge_datasets(dataset_folder_ready, ratings_file)


    

    




INFO:root:dataset was already downloaded
INFO:root:dataset stored in: /Users/hcorona/github/recsys-101-workshop/data/ml-latest-small
INFO:root:loaded 44 personal ratings
INFO:root:loaded 9125 movies
INFO:root:loaded 100048 ratings in total

In [4]:

    

# the data is stored in a long pandas dataframe
# we need to pivot the data to create a [user x movie] matrix
ratings_matrix = ratings.pivot_table(index='customer', columns='movie', values='rating', fill_value=0)
ratings_matrix = ratings_matrix.transpose()


    

In [5]:

    

# number of users, items and ratings
n_users= len(ratings['customer'].unique())
n_items= len(ratings['movie'].unique())
n_ratings = ratings.shape[0]

print('number of users',n_users)
print('number of items', n_items)
print('number of ratings', n_ratings)


    

    




number of users 672
number of items 9064
number of ratings 100048

In [6]:

    

# Plot distribution of ratings using the pandas plot functionality
histogram = ratings['rating'].hist()
histogram.set_xlabel('number of ratings')
histogram.set_ylabel('rating value')


    

    
Out[6]:





<matplotlib.text.Text at 0x1099cc400>






    

In [7]:

    

# movie popularity distribution
popularity = pd.DataFrame(ratings['movie'].value_counts())
popularity.sort_values(by='movie', ascending=False)
popularity.plot(figsize=(15, 5), title="Movie Popularity").grid(True)


    

In [8]:

    

# movie popularity distribution: zoom into the top-100 movies
popularity[0:100].plot(figsize=(15, 5), title="Movie Popularity").grid(True)