Objective: to run association rules on a binary values dataset
d = {'col1': [0, 0,1], 'col2': [1, 0,0], 'col3': [0,1,1]} df = pd.DataFrame(data=d) This produces a data frame with 0's and 1's for corresponding column values.
The problem is when I make use of code like the following:
from mlxtend.frequent_patterns import apriori from mlxtend.frequent_patterns import association_rules frequent_itemsets = apriori(pattern_dataset, min_support=0.50,use_colnames=True) rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1) rules Typically this runs just fine, but in running it this time I have encountered an error.
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-61-46ec6f572255> in <module>() 4 frequent_itemsets = apriori(pattern_dataset, min_support=0.50,use_colnames=True) 5 frequent_itemsets ----> 6 rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1) 7 rules D:\AnaConda\lib\site-packages\mlxtend\frequent_patterns\association_rules.py in association_rules(df, metric, min_threshold, support_only) 127 values = df['support'].values 128 frozenset_vect = np.vectorize(lambda x: frozenset(x)) --> 129 frequent_items_dict = dict(zip(frozenset_vect(keys), values)) 130 131 # prepare buckets to collect frequent rules D:\AnaConda\lib\site-packages\numpy\lib\function_base.py in __call__(self, *args, **kwargs) 1970 vargs.extend([kwargs[_n] for _n in names]) 1971 -> 1972 return self._vectorize_call(func=func, args=vargs) 1973 1974 def _get_ufunc_and_otypes(self, func, args): D:\AnaConda\lib\site-packages\numpy\lib\function_base.py in _vectorize_call(self, func, args) 2040 res = func() 2041 else: -> 2042 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args) 2043 2044 # Convert args to object arrays first D:\AnaConda\lib\site-packages\numpy\lib\function_base.py in _get_ufunc_and_otypes(self, func, args) 1996 args = [asarray(arg) for arg in args] 1997 if builtins.any(arg.size == 0 for arg in args): -> 1998 raise ValueError('cannot call `vectorize` on size 0 inputs ' 1999 'unless `otypes` is set') 2000 ValueError: cannot call `vectorize` on size 0 inputs unless `otypes` is set This is what I have for dtypes in Pandas, any help would be appreciated.
col1 int64 col2 int64 col3 int64 dtype: object 2 Answers
128 frozenset_vect = np.vectorize(lambda x: frozenset(x)) --> 129 frequent_items_dict = dict(zip(frozenset_vect(keys), values)) Here np.vectorize wraps the frozenset(x) function in code that can take an array or list (keys), and pass each element for evaluation. It a kind of numpy iteration (convenient, but not fast). But to determine what kind (dtype) of array it returns it performs a test run with the first element of keys. An alternative to doing this test run is to use the otypes parameter.
Anyways, in this particular run, keys is evidently empty, a 0 size array or list. It could return an equivalent shape result array, but it still has to set a dtype. Hence the error.
Evidently the code writer never anticipated the case where keys was empty. So you need to tackle the question of why is it empty?
We need to look at the association_rules code see how keys is set. Its use in line 129 suggests that it has the same number of elements as values, which is derived from the df with:
values = df['support'].values If keys has 0 elements, then values does as well, and df has 0 'rows'.
What the size of frequent_itemsets?
I add a mlxtend tag because the error arises during the use of its code. You/we need to examine that code or its documentation to determine why this dataframe is empty.
Workaround:
def encode_units(x): if x <= 0: return 0 if x >= 1: return 1 yourdataset_sets = yourdataset.applymap(encode_units) frequent_itemsets = apriori(yourdataset_sets, min_support=0.001, use_colnames=True) rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1) Credit: saeedesmaili
