step-3.1.py

from IPython.display import Image, display
import networkx as nx
import numpy as np
import time
import datetime


#get all users that I am directly following
api.getSelfUsersFollowing()
result = api.LastJson
follow_relationships = []
for user in tqdm(result['users']):
    followed_user_id = user['pk'] 
    followed_user_name = user['full_name']
    follow_relationships.append((user_id, followed_user_id, me, followed_user_name))
  
df_local = pd.DataFrame(follow_relationships, columns=['src_id','dst_id', 'src_name', 'dst_name'])
all_user_ids_local = np.unique(df_local[['src_id', 'dst_id']].values.reshape(1,-1))



#grab all my likes from the past year
last_year = datetime.datetime.now() - datetime.timedelta(days=365)
now = datetime.datetime.now()
last_result_time = now
all_likes = []
max_id = 0

while last_result_time > last_year:
    api.getLikedMedia(maxid=max_id)
    results = api.LastJson
    [all_likes.append(item) for item in results['items']]
    max_id = results['items'][-1]['pk']
    last_result_time = pd.to_datetime(results['items'][-1]['taken_at'], unit='s')

like_counts = pd.Series([i['user']['pk'] for i in all_likes]).value_counts()   


#calculate number of times I've liked each users post
for i in tqdm(like_counts.index):
    if i in df_local['dst_id'].values: # only count likes from people I follow (naive but simple)
        ind = df_local[(df_local['src_id'] == user_id) & (df_local['dst_id'] == i)].index[0]
        if like_counts[i] is not None:
            df_local = df_local.set_value(ind,'weight',like_counts[i])
ind = df_local[df_local['weight'].isnull()].index
df_local = df_local.set_value(ind,'weight',0.5)

#create social graph and calculate pagerank
G = nx.from_pandas_dataframe(df_local, 'src_id', 'dst_id')
#calculate personalized pagerank
perzonalization_dict = dict(zip(G.nodes(), [0]*len(G.nodes())))
perzonalization_dict[user_id] = 1
ppr = nx.pagerank(G, personalization=perzonalization_dict)

#this may take a while if you follow a lot of people
urls = []
taken_at = []
num_likes = []
num_comments = []
page_rank = []
users = []
weight = []
for user_id in tqdm(all_user_ids_local):
    api.getUserFeed(user_id)
    result = api.LastJson
    if 'items' in result.keys():
        for item in result['items']:
            if 'image_versions2' in item.keys(): #only grabbing pictures (no videos or carousels)
                # make sure we can grab keys before trying to append
                
                url = item['image_versions2']['candidates'][1]['url']
                taken = item['taken_at']
                try:
                    likes = item['like_count']
                except KeyError:
                    likes = 0
                try:
                    comments = item['comment_count']
                except KeyError:
                    comments = 0
                
                pr = ppr[item['user']['pk']]
                user = item['user']['full_name']
                if user != me: #don't count myself!
                    urls.append(url)
                    taken_at.append(taken)
                    num_likes.append(likes)
                    num_comments.append(comments)
                    page_rank.append(pr)
                    users.append(user)
                    weight.append(df_local[df_local['dst_name'] == user]['weight'].values[0])

    
#now we can make a dataframe with all of that information  
scores_df = pd.DataFrame(
    {'urls': urls,
     'taken_at': taken_at,
     'num_likes': num_likes,
     'num_comments': num_comments,
     'page_rank': page_rank,
     'users': users,
     'weight': weight
    })
#don't care about anything older than 1 week
oldest_time = int((datetime.datetime.now()
                   - datetime.timedelta(weeks = 1)).strftime('%s'))

scores_df = scores_df[scores_df['taken_at'] > oldest_time]

# /1e5 to help out with some machine precision (numbers get real small otherwise)
scores_df['time_score'] = np.exp(-(int(time.time()) - scores_df['taken_at'])/1e5)

scores_df['total_score'] = (np.log10(scores_df['num_comments']+2) * np.log10(scores_df['num_likes']+1)
                            * scores_df['page_rank'] * scores_df['time_score']
                            * np.log(scores_df['weight']+1))

        
# calculate top ten highest rated posts
top_ten = scores_df['total_score'].nlargest(10)
top_rows = scores_df.loc[top_ten.index].values
top_personal_img = []


top_graph_img = []
#display the feed
for row in top_rows:
    img = Image(row[4], format='jpeg')
    top_graph_img.append(img)
    display(img)
    top_personal_img.append(img)
    print('taken_at: %s' % time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(row[3])) )
    print('number of likes: %s' % row[1])
    print('number of comments: %s' % row[0])
    print('page_rank: %s' % row[2])
    print(row[5])