MeanInvUserFreq

class rectools.metrics.novelty.MeanInvUserFreq(k: int)[source]

Bases: MetricAtK

Mean Inverse User Frequency metric.

Estimate mean novelty of items in recommendations, where “novelty” of item is inversely proportional to the number of users who interacted with it.

\[MIUF@k = -(\sum_{i=1}^{k} \log_{2} (users(i) / n\_users)) / k\]

where - users(i) is number of users that previously interacted with item with rank i. - n_users is the overall number of users in previous interactions.

Parameters: k (int) – Number of items at the top of recommendations list that will be used to calculate metric.

Examples

>>> reco = pd.DataFrame(
...     {
...         Columns.User: [1, 2, 2, 3, 3],
...         Columns.Item: [3, 2, 3, 1, 2],
...         Columns.Rank: [1, 1, 2, 1, 2],
...     }
... )
>>> prev_interactions = pd.DataFrame(
...     {
...         Columns.User: [1, 1, 2, 3],
...         Columns.Item: [1, 2, 1, 1],
...     }
... )
>>> MeanInvUserFreq(k=1).calc_per_user(reco, prev_interactions).values
array([1.5849625, 1.5849625, 0. ])
>>> MeanInvUserFreq(k=3).calc_per_user(reco, prev_interactions).values
array([1.5849625 , 1.5849625 , 0.79248125])

Inherited-members
Parameters: k (int) –

Methods

`calc`(reco, prev_interactions)	Calculate metric value.
`calc_from_fitted`(fitted)	Calculate metric value from fitted data.
`calc_per_user`(reco, prev_interactions)	Calculate metric values for all users.
`calc_per_user_from_fitted`(fitted)	Calculate metric values for all users from fitted data.
`fit`(reco, prev_interactions, k_max)	Prepare intermediate data for effective calculation.

Attributes

calc(reco: DataFrame, prev_interactions: DataFrame) → float[source]

Calculate metric value.

Parameters

reco (pd.DataFrame) – Recommendations table with columns Columns.User, Columns.Item, Columns.Rank.
prev_interactions (pd.DataFrame) – Table with previous user-item interactions, with columns Columns.User, Columns.Item.

Returns

Value of metric (average between users).

Return type

float

calc_from_fitted(fitted: MIUFFitted) → float[source]

Calculate metric value from fitted data.

For parameters used result of fit method.

Parameters: fitted (MIUFFitted) – Meta data that got from .fit method.
Returns: Value of metric (average between users).
Return type: float

calc_per_user(reco: DataFrame, prev_interactions: DataFrame) → Series[source]

Calculate metric values for all users.

Parameters

reco (pd.DataFrame) – Recommendations table with columns Columns.User, Columns.Item, Columns.Rank.
prev_interactions (pd.DataFrame) – Table with previous user-item interactions, with columns Columns.User, Columns.Item.

Returns

Values of metric (index - user id, values - metric value for every user).

Return type

pd.Series

calc_per_user_from_fitted(fitted: MIUFFitted) → Series[source]

Calculate metric values for all users from fitted data.

For parameters used result of fit method.

Parameters: fitted (MIUFFitted) – Meta data that got from .fit method.
Returns: Values of metric (index - user id, values - metric value for every user).
Return type: pd.Series

classmethod fit(reco: DataFrame, prev_interactions: DataFrame, k_max: int) → MIUFFitted[source]

Prepare intermediate data for effective calculation.

You can use this method to prepare some intermediate data for later calculation. It can optimize calculations if you want calculate metric for different values of k.

Parameters

reco (pd.DataFrame) – Recommendations table with columns Columns.User, Columns.Item, Columns.Rank.
prev_interactions (pd.DataFrame) – Table with previous user-item interactions, with columns Columns.User, Columns.Item.
k_max (int) – k is number of items at the top of recommendations list that will be used to calculate metric. So k_max is maximum value of k parameter for which you want to calculate metric.

Return type

MIUFFitted