import argparse
import json
import numpy as np

from compute_scores import pearson_score

def build_arg_parser():
    parser = argparse.ArgumentParser(description='Find users who are similar to the input user')
    parser.add_argument('--user', dest='user', required=True,
            help='Input user')
    return parser

# Finds users in the dataset that are similar to the input user 
def find_similar_users(dataset, user, num_users):
    if user not in dataset:
        raise TypeError('Cannot find ' + user + ' in the dataset')

    # Compute Pearson score between input user 
    # and all the users in the dataset
    scores = np.array([[x, pearson_score(dataset, user, 
            x)] for x in dataset if x != user])

    # Sort the scores in decreasing order
    scores_sorted = np.argsort(scores[:, 1])[::-1]

    # Extract the top 'num_users' scores
    top_users = scores_sorted[:num_users] 

    return scores[top_users] 

if __name__=='__main__':
    args = build_arg_parser().parse_args()
    user = args.user

    ratings_file = 'ratings.json'

    with open(ratings_file, 'r') as f:
        data = json.loads(f.read())

    print('\nUsers similar to ' + user + ':\n')
    similar_users = find_similar_users(data, user, 3) 
    print('User\t\t\tSimilarity score')
    print('-'*41)
    for item in similar_users:
        print(item[0], '\t\t', round(float(item[1]), 2))
import argparse
import json
import numpy as np

from compute_scores import pearson_score

def build_arg_parser():
    parser = argparse.ArgumentParser(description='Find users who are similar to the input user')
    parser.add_argument('--user', dest='user', required=True,
            help='Input user')
    return parser

# Finds users in the dataset that are similar to the input user 
def find_similar_users(dataset, user, num_users):
    if user not in dataset:
        raise TypeError('Cannot find ' + user + ' in the dataset')

    # Compute Pearson score between input user 
    # and all the users in the dataset
    scores = np.array([[x, pearson_score(dataset, user, 
            x)] for x in dataset if x != user])

    # Sort the scores in decreasing order
    scores_sorted = np.argsort(scores[:, 1])[::-1]

    # Extract the top 'num_users' scores
    top_users = scores_sorted[:num_users] 

    return scores[top_users] 

if __name__=='__main__':
    args = build_arg_parser().parse_args()
    user = args.user

    ratings_file = 'ratings.json'

    with open(ratings_file, 'r') as f:
        data = json.loads(f.read())

    print('\nUsers similar to ' + user + ':\n')
    similar_users = find_similar_users(data, user, 3) 
    print('User\t\t\tSimilarity score')
    print('-'*41)
    for item in similar_users:
        print(item[0], '\t\t', round(float(item[1]), 2))
# Finds users in the dataset that are similar to the input user # Compute Pearson score between input user # and all the users in the dataset # Sort the scores in decreasing order # Extract the top 'num_users' scores