Apriori algorithm using Pandas I want to optimize my Apriori algorithm for speed:

from itertools import combinations
import pandas as pd
import numpy as np

trans=pd.read_table('output.txt', header=None,index_col=0)

def apriori(trans, support=0.01, minlen=1):
    ts=pd.get_dummies(trans.unstack().dropna()).groupby(level=1).sum()

    collen, rowlen  =ts.shape

    #-------------Max leng (not used)
    #tssum=ts.sum(axis=1)
    #maxlen=int(tssum.loc[tssum.idxmax()])


    pattern = []
    for cnum in range(minlen, rowlen+1):
        for cols in combinations(ts, cnum):
            patsup = ts[list(cols)].all(axis=1).sum()
            patsup=float(patsup)/collen
            pattern.append([",".join(cols), patsup])
    sdf = pd.DataFrame(pattern, columns=["Pattern", "Support"])
    results=sdf[sdf.Support &gt;= support]

    return results


When you input a dataframe of transactions:




&gt;&gt;&gt; trans
        1  2    3    4
0                     
11      a  b    c  NaN
666     a  d    e  NaN
10101   b  c    d  NaN
1010    a  b    c    d
414147  b  c  NaN  NaN
10101   a  b    d  NaN
1242    d  e  NaN  NaN
101     a  b    c  NaN
411     c  d    e  NaN
444     a  b    c  NaN

[10 rows x 4 columns]



It will yield:


Ap=apriori(trans)
print Ap
&gt;&gt;&gt; 
   Pattern  Support
0        a      0.6
1        b      0.7
2        c      0.7
3        d      0.6
4        e      0.3
5      a,b      0.5
6      a,c      0.4
7      a,d      0.3
8      a,e      0.1
9      b,c      0.6
10     b,d      0.3
12     c,d      0.3
13     c,e      0.1
14     d,e      0.3
15   a,b,c      0.4
16   a,b,d      0.2
18   a,c,d      0.1
20   a,d,e      0.1
21   b,c,d      0.2
24   c,d,e      0.1



I want to know if this can be optimized further so that it can run faster on large datasets.  I also want to know if there a way to use purely Pandas without combinations from itertools.
First off, this is just a part of the Apriori algorithm. Here, you're just finding frequent itemsets. Apriori continues to find association rules in those itemsets.

Also, using combinations() like this is not optimal. For example, if we know that the combination AB does not enjoy reasonable support, we do not need to consider any combination that contains AB anymore (ABC, ABD, etc. will all be infrequent as well). Your algorithm does not take this into consideration.

This means that your algorithm will check all the possible combinations (2n, where n is the number of possible items), while in fact we can prune the search tree as above and reduce this complexity drastically (depending on the density of the dataset).