How Can I Match All The Key Value Pair In Python Which Running Too Long

User-item affinity and recommendations : I am creating a table which suggests 'customers who bought this item also bought algorithm ' Input dataset productId userId Prod1

Solution 1:

Yes, algorithm could be improved. You are recalculating user list for items in inside loop multiple times. You can just get a dictionary of item and their users outside loops.

# get unique items
items = set(main.productId)

n_users = len(set(main.userId))

# make a dictionary of item and users who bought that item
item_users = main.groupby('productId')['userId'].apply(set).to_dict()

# iterate over combinations of item1 and item2 and store scores
result = []
for item1, item2 in itertools.combinations(items, 2):

  score = len(item_users[item1] & item_users[item2]) / n_users
  item_tuples = [(item1, item2), (item2, item1)]
  result.append((item1, item2, score))
  result.append((item2, item1, score)) # store score for reverse order as well# convert results to a dataframe
result = pd.DataFrame(result, columns=["item1", "item2", "score"])

Timing differences:

Original implementation from question

# 3 loops, best of 3: 41.8 ms per loop

Mark's Method 2

# 3 loops, best of 3: 19.9 ms per loop

Implementation in this answer

# 3 loops, best of 3: 3.01 ms per loop

Solution 2:

The key here is to create a cartesian product of productId. See code below,

Method 1(works with smaller dataset)

result=(main.drop_duplicates(['productId','userId'])
            .assign(cartesian_key=1)
            .pipe(lambda x:x.merge(x,on='cartesian_key'))
            .drop('cartesian_key',axis=1)
            .loc[lambda x:(x.productId_x!=x.productId_y) & (x.userId_x==x.userId_y)]
            .groupby(['productId_x','productId_y']).size()
            .div(data['userId'].nunique()))

result

Prod1   prod2   0.75
Prod1   prod3   0.75
Prod1   prod4   0.75
Prod1   prod5   0.5
prod2   Prod1   0.75
prod2   prod3   0.5
prod2   prod4   0.5
prod2   prod5   0.25
prod3   Prod1   0.75
prod3   prod2   0.5
prod3   prod4   0.5
prod3   prod5   0.5
prod4   Prod1   0.75
prod4   prod2   0.5
prod4   prod3   0.5
prod4   prod5   0.5
prod5   Prod1   0.5
prod5   prod2   0.25
prod5   prod3   0.5
prod5   prod4   0.5

Method 2

result = (df.groupby(['productId','userId']).size()
            .clip(upper=1)
            .unstack()
            .assign(key=1)
            .reset_index()
            .pipe(lambda x:x.merge(x,on='key'))
            .drop('key',axis=1)
            .loc[lambda x:(x.productId_x!=x.productId_y)]
            .set_index(['productId_x','productId_y'])
            .pipe(lambda x:x.set_axis(x.columns.str.split('_',expand=True),axis=1,inplace=False))
            .swaplevel(axis=1)
            .pipe(lambda x:(x['x']+x['y']))
            .fillna(0)
            .div(2) 
            .mean(axis=1))