MaxDiffArrays#

class pymc_marketing.customer_choice.maxdiff.MaxDiffArrays[source]#

Preprocessed arrays ready for the MaxDiff likelihood.

Attributes:

item_idxnp.ndarray: Shape (T, K_max) int64. Items shown in each task as indices into the item pool, padded with the reference item’s index where mask is False.
masknp.ndarray: Shape (T, K_max) bool. True where the position is a real shown item.
best_posnp.ndarray: Shape (T,) int64. Position (0..K_max-1) of the best-chosen item.
worst_posnp.ndarray: Shape (T,) int64. Position of the worst-chosen item.
resp_idxnp.ndarray: Shape (T,) int64. Respondent index for each task.
n_tasksint: Number of tasks T.
n_respondentsint: Number of unique respondents.
n_itemsint: Size of the full item pool.
k_maxint: Maximum subset size across all tasks.
item_to_idxdict[str, int]: Mapping from item name to integer index.
respondent_to_idxdict[Any, int]: Mapping from respondent identifier to integer index.

Methods

`MaxDiffArrays.__init__`(args, *kwargs)
`MaxDiffArrays.clear`()
`MaxDiffArrays.copy`()
`MaxDiffArrays.fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`MaxDiffArrays.get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`MaxDiffArrays.items`()
`MaxDiffArrays.keys`()
`MaxDiffArrays.pop`(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`MaxDiffArrays.popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`MaxDiffArrays.setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`MaxDiffArrays.update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`MaxDiffArrays.values`()

Attributes