def test_on_df_with_missing_entries():
# this is a data frame where information about
# antecedents and consequents have been cropped
# see https://github.com/rasbt/mlxtend/issues/390
# for more details
dict = {'itemsets': [['177', '176'], ['177', '179'],
['176', '178'], ['176', '179'],
['93', '100'], ['177', '178'],
['177', '176', '178']],
'support': [0.253623, 0.253623, 0.217391,
0.217391, 0.181159, 0.108696, 0.108696]}
df = pd.DataFrame(dict)
numpy_assert_raises(KeyError, association_rules, df)
def test_on_df_with_missing_entries():
# this is a data frame where information about
# antecedents and consequents have been cropped
# see https://github.com/rasbt/mlxtend/issues/390
# for more details
dict = {
'itemsets': [['177', '176'], ['177', '179'], ['176', '178'],
['176', '179'], ['93', '100'], ['177', '178'],
['177', '176', '178']],
'support':
[0.253623, 0.253623, 0.217391, 0.217391, 0.181159, 0.108696, 0.108696]
}
df = pd.DataFrame(dict)
numpy_assert_raises(KeyError, association_rules, df)
def test_wrong_metric():
numpy_assert_raises(ValueError, association_rules, df_freq_items,
'unicorn')
def test_no_itemsets_col():
df_no_itemsets_col = df_freq_items.loc[:, ['support']]
numpy_assert_raises(ValueError, association_rules, df_no_itemsets_col)
def test_wrong_metric():
numpy_assert_raises(ValueError, association_rules,
df_freq_items, 'unicorn')
def test_no_itemsets_col():
df_no_itemsets_col = df_freq_items.loc[:, ['support']]
numpy_assert_raises(ValueError, association_rules, df_no_itemsets_col)