SimpleClassificationMetric

class rectools.metrics.classification.SimpleClassificationMetric(k: int, debias_config: Optional[DebiasConfig] = None)[source]

Bases: DebiasableMetrikAtK

Simple classification metric base class.

Warning: This class should not be used directly. Use derived classes instead.

Parameters

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

  • debias_config (DebiasConfig, optional, default None) – Config with debias method parameters (iqr_coef, random_state).

Inherited-members

Methods

calc(reco, interactions)

Calculate metric value.

calc_from_confusion_df(confusion_df[, ...])

Calculate metric value from prepared confusion matrix.

calc_per_user(reco, interactions)

Calculate metric values for all users.

calc_per_user_from_confusion_df(confusion_df)

Calculate metric values for all users from prepared confusion matrix.

Attributes

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

Calculate metric value.

Parameters

  • reco (pd.DataFrame) – Recommendations table with columns Columns.User, Columns.Item, Columns.Rank.

  • interactions (pd.DataFrame) – Interactions table with columns Columns.User, Columns.Item.

Returns

Value of metric (average between users).

Return type

float

calc_from_confusion_df(confusion_df: DataFrame, is_debiased: bool = False) → float[source]

Calculate metric value from prepared confusion matrix.

Parameters

  • confusion_df (pd.DataFrame) – Table with some confusion values for every user. Columns are: Columns.User, LIKED, TP, FP, FN. This table can be generated by make_confusions (or calc_confusions) function. See its description for details.

  • is_debiased (bool, default False) – An indicator of whether the debias transformation has been applied before or not.

Returns

Value of metric (average between users).

Return type

float

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

Calculate metric values for all users.

Parameters

  • reco (pd.DataFrame) – Recommendations table with columns Columns.User, Columns.Item, Columns.Rank.

  • interactions (pd.DataFrame) – Interactions table 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_confusion_df(confusion_df: DataFrame, is_debiased: bool = False) → Series[source]

Calculate metric values for all users from prepared confusion matrix.

Parameters

  • confusion_df (pd.DataFrame) – Table with some confusion values for every user. Columns are: Columns.User, LIKED, TP, FP, FN. This table can be generated by make_confusions (or calc_confusions) function. See its description for details.

  • is_debiased (bool, default False) – An indicator of whether the debias transformation has been applied before or not.

Returns

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

Return type

pd.Series