mlxtend version: 0.23.1

apriori

apriori(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0, low_memory=False)

Get frequent itemsets from a one-hot DataFrame

Parameters

  • df : pandas DataFrame

    pandas DataFrame the encoded format. Also supports DataFrames with sparse data; for more info, please see (https://pandas.pydata.org/pandas-docs/stable/ user_guide/sparse.html#sparse-data-structures)

    Please note that the old pandas SparseDataFrame format is no longer supported in mlxtend >= 0.17.2.

    The allowed values are either 0/1 or True/False. For example,

    Apple Bananas Beer Chicken Milk Rice 0 True False True True False True 1 True False True False False True 2 True False True False False False 3 True True False False False False 4 False False True True True True 5 False False True False True True 6 False False True False True False 7 True True False False False False

  • min_support : float (default: 0.5)

    A float between 0 and 1 for minumum support of the itemsets returned. The support is computed as the fraction transactions_where_item(s)_occur / total_transactions.

  • use_colnames : bool (default: False)

    If True, uses the DataFrames' column names in the returned DataFrame instead of column indices.

  • max_len : int (default: None)

    Maximum length of the itemsets generated. If None (default) all possible itemsets lengths (under the apriori condition) are evaluated.

  • verbose : int (default: 0)

    Shows the number of iterations if >= 1 and low_memory is True. If

    =1 and low_memory is False, shows the number of combinations.

  • low_memory : bool (default: False)

    If True, uses an iterator to search for combinations above min_support. Note that while low_memory=True should only be used for large dataset if memory resources are limited, because this implementation is approx. 3-6x slower than the default.

Returns

pandas DataFrame with columns ['support', 'itemsets'] of all itemsets that are >= min_support and < than max_len (if max_len is not None). Each itemset in the 'itemsets' column is of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see https://docs.python.org/3.6/library/stdtypes.html#frozenset).

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/

association_rules

association_rules(df, metric='confidence', min_threshold=0.8, support_only=False)

Generates a DataFrame of association rules including the metrics 'score', 'confidence', and 'lift'

Parameters

  • df : pandas DataFrame

    pandas DataFrame of frequent itemsets with columns ['support', 'itemsets']

  • metric : string (default: 'confidence')

    Metric to evaluate if a rule is of interest. Automatically set to 'support' if support_only=True. Otherwise, supported metrics are 'support', 'confidence', 'lift',

'leverage', 'conviction' and 'zhangs_metric' These metrics are computed as follows:

- support(A->C) = support(A+C) [aka 'support'], range: [0, 1]

- confidence(A->C) = support(A+C) / support(A), range: [0, 1]

- lift(A->C) = confidence(A->C) / support(C), range: [0, inf]

- leverage(A->C) = support(A->C) - support(A)*support(C),
range: [-1, 1]

- conviction = [1 - support(C)] / [1 - confidence(A->C)],
range: [0, inf]

- zhangs_metric(A->C) =
leverage(A->C) / max(support(A->C)*(1-support(A)), support(A)*(support(C)-support(A->C)))
range: [-1,1]

  • min_threshold : float (default: 0.8)

    Minimal threshold for the evaluation metric, via the metric parameter, to decide whether a candidate rule is of interest.

  • support_only : bool (default: False)

    Only computes the rule support and fills the other metric columns with NaNs. This is useful if:

    a) the input DataFrame is incomplete, e.g., does not contain support values for all rule antecedents and consequents

    b) you simply want to speed up the computation because you don't need the other metrics.

Returns

pandas DataFrame with columns "antecedents" and "consequents" that store itemsets, plus the scoring metric columns: "antecedent support", "consequent support", "support", "confidence", "lift", "leverage", "conviction" of all rules for which metric(rule) >= min_threshold. Each entry in the "antecedents" and "consequents" columns are of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see https://docs.python.org/3.6/library/stdtypes.html#frozenset).

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/

fpgrowth

fpgrowth(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)

Get frequent itemsets from a one-hot DataFrame

Parameters

  • df : pandas DataFrame

    pandas DataFrame the encoded format. Also supports DataFrames with sparse data; for more info, please see https://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#sparse-data-structures.

    Please note that the old pandas SparseDataFrame format is no longer supported in mlxtend >= 0.17.2.

    The allowed values are either 0/1 or True/False. For example,

    Apple Bananas Beer Chicken Milk Rice 0 True False True True False True 1 True False True False False True 2 True False True False False False 3 True True False False False False 4 False False True True True True 5 False False True False True True 6 False False True False True False 7 True True False False False False

  • min_support : float (default: 0.5)

    A float between 0 and 1 for minimum support of the itemsets returned. The support is computed as the fraction transactions_where_item(s)_occur / total_transactions.

  • use_colnames : bool (default: False)

    If true, uses the DataFrames' column names in the returned DataFrame instead of column indices.

  • max_len : int (default: None)

    Maximum length of the itemsets generated. If None (default) all possible itemsets lengths are evaluated.

  • verbose : int (default: 0)

    Shows the stages of conditional tree generation.

Returns

pandas DataFrame with columns ['support', 'itemsets'] of all itemsets that are >= min_support and < than max_len (if max_len is not None). Each itemset in the 'itemsets' column is of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see https://docs.python.org/3.6/library/stdtypes.html#frozenset).

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpgrowth/

fpmax

fpmax(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)

Get maximal frequent itemsets from a one-hot DataFrame

Parameters

  • df : pandas DataFrame

    pandas DataFrame the encoded format. Also supports DataFrames with sparse data; for more info, please see (https://pandas.pydata.org/pandas-docs/stable/ user_guide/sparse.html#sparse-data-structures)

    Please note that the old pandas SparseDataFrame format is no longer supported in mlxtend >= 0.17.2.

    The allowed values are either 0/1 or True/False. For example,

    Apple Bananas Beer Chicken Milk Rice 0 True False True True False True 1 True False True False False True 2 True False True False False False 3 True True False False False False 4 False False True True True True 5 False False True False True True 6 False False True False True False 7 True True False False False False

  • min_support : float (default: 0.5)

    A float between 0 and 1 for minimum support of the itemsets returned. The support is computed as the fraction transactions_where_item(s)_occur / total_transactions.

  • use_colnames : bool (default: False)

    If true, uses the DataFrames' column names in the returned DataFrame instead of column indices.

  • max_len : int (default: None)

    Given the set of all maximal itemsets, return those that are less than max_len. If None (default) all possible itemsets lengths are evaluated.

  • verbose : int (default: 0)

    Shows the stages of conditional tree generation.

Returns

pandas DataFrame with columns ['support', 'itemsets'] of all maximal itemsets that are >= min_support and < than max_len (if max_len is not None). Each itemset in the 'itemsets' column is of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see https://docs.python.org/3.6/library/stdtypes.html#frozenset).

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpmax/

hmine

hmine(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0) -> pandas.core.frame.DataFrame

Get frequent itemsets from a one-hot DataFrame

Parameters

  • df : pandas DataFrame

    pandas DataFrame the encoded format. Also supports DataFrames with sparse data; for more info, please see https://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#sparse-data-structures.

    Please note that the old pandas SparseDataFrame format is no longer supported in mlxtend >= 0.17.2.

    The allowed values are either 0/1 or True/False. For example,

    Apple Bananas Beer Chicken Milk Rice 0 True False True True False True 1 True False True False False True 2 True False True False False False 3 True True False False False False 4 False False True True True True 5 False False True False True True 6 False False True False True False 7 True True False False False False

  • min_support : float (default: 0.5)

    A float between 0 and 1 for minimum support of the itemsets returned. The support is computed as the fraction transactions_where_item(s)_occur / total_transactions.

  • use_colnames : bool (default: False)

    If true, uses the DataFrames' column names in the returned DataFrame instead of column indices.

  • max_len : int (default: None)

    Maximum length of the itemsets generated. If None (default) all possible itemsets lengths are evaluated.

  • verbose : int (default: 0)

    Shows the stages of conditional tree generation.

Returns

pandas DataFrame with columns ['support', 'itemsets'] of all itemsets that are >= min_support and < than max_len (if max_len is not None). Each itemset in the 'itemsets' column is of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see https://docs.python.org/3.6/library/stdtypes.html#frozenset).

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/hmine/