Evaluation Methods¶

Base Method¶

class cornac.eval_methods.base_method.BaseMethod(data=None, fmt='UIR', rating_threshold=1.0, seed=None, exclude_unknowns=True, verbose=False, **kwargs)[source]¶

Base Evaluation Method

Parameters:

data (array-like, required) – Raw preference data in the triplet format [(user_id, item_id, rating_value)].
rating_threshold (float, optional, default: 1.0) – Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected).
seed (int, optional, default: None) – Random seed for reproducibility.
exclude_unknowns (bool, optional, default: True) – If True, unknown users and items will be ignored during model evaluation.
verbose (bool, optional, default: False) – Output running log.

add_modalities(**kwargs)[source]¶: Add successfully built modalities to all datasets. This is handy for seperately built modalities that are not invoked in the build method.

evaluate(model, metrics, user_based, show_validation=True)[source]¶

Evaluate given models according to given metrics

Parameters:	model (`cornac.models.Recommender`) – Recommender model to be evaluated. metrics (`iterable`) – List of metrics. user_based (bool, required) – Evaluation strategy for the rating metrics. Whether results are averaging based on number of users or number of ratings. show_validation (bool, optional, default: True) – Whether to show the results on validation set (if exists).
Returns:	res
Return type:	`cornac.experiment.Result`

classmethod from_splits(train_data, test_data, val_data=None, fmt='UIR', rating_threshold=1.0, exclude_unknowns=False, seed=None, verbose=False, **kwargs)[source]¶

Constructing evaluation method given data.

Parameters:	train_data (array-like) – Training data test_data (array-like) – Test data val_data (array-like, optional, default: None) – Validation data fmt (str, default: 'UIR') – Format of the input data. Currently, we are supporting: ’UIR’: User, Item, Rating ‘UIRT’: User, Item, Rating, Timestamp rating_threshold (float, default: 1.0) – Threshold to decide positive or negative preferences. exclude_unknowns (bool, default: False) – Whether to exclude unknown users/items in evaluation. seed (int, optional, default: None) – Random seed for reproduce the splitting. verbose (bool, default: False) – The verbosity flag.
Returns:	method – Evaluation method object.
Return type:	`<cornac.eval_methods.BaseMethod>`

cornac.eval_methods.base_method.ranking_eval(model, metrics, train_set, test_set, val_set=None, rating_threshold=1.0, exclude_unknowns=True, verbose=False)[source]¶

Evaluate model on provided ranking metrics.

Parameters:

model (cornac.models.Recommender, required) – Recommender model to be evaluated.
metrics (iterable, required) – List of rating metrics cornac.metrics.RankingMetric.
train_set (cornac.data.Dataset, required) – Dataset to be used for model training. This will be used to exclude observations already appeared during training.
test_set (cornac.data.Dataset, required) – Dataset to be used for evaluation.
val_set (cornac.data.Dataset, optional, default: None) – Dataset to be used for model selection. This will be used to exclude observations already appeared during validation.
rating_threshold (float, optional, default: 1.0) – The threshold to convert ratings into positive or negative feedback.
exclude_unknowns (bool, optional, default: True) – Ignore unknown users and items during evaluation.
verbose (bool, optional, default: False) – Output evaluation progress.

Returns:

res –

Tuple of two lists:

average result for each of the metrics
average result per user for each of the metrics

Return type:

(List, List)

cornac.eval_methods.base_method.rating_eval(model, metrics, test_set, user_based=False, verbose=False)[source]¶

Evaluate model on provided rating metrics.

Parameters:

model (cornac.models.Recommender, required) – Recommender model to be evaluated.
metrics (iterable, required) – List of rating metrics cornac.metrics.RatingMetric.
test_set (cornac.data.Dataset, required) – Dataset to be used for evaluation.
user_based (bool, optional, default: False) – Evaluation mode. Whether results are averaging based on number of users or number of ratings.
verbose (bool, optional, default: False) – Output evaluation progress.

Returns:

res –

Tuple of two lists:

average result for each of the metrics
average result per user for each of the metrics

Return type:

(List, List)

Cross Validation¶

class cornac.eval_methods.cross_validation.CrossValidation(data, n_folds=5, rating_threshold=1.0, partition=None, seed=None, exclude_unknowns=True, verbose=False, **kwargs)[source]¶

Cross Validation Evaluation Method.

Parameters:

data (array-like, required) – Raw preference data in the triplet format [(user_id, item_id, rating_value)].
n_folds (int, optional, default: 5) – The number of folds for cross validation.
rating_threshold (float, optional, default: 1.0) – Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected).
partition (array-like, shape (n_observed_ratings,), optional, default: None) – The partition of ratings into n_folds (fold label of each rating) If None, random partitioning is performed to assign each rating into a fold.
seed (int, optional, default: None) – Random seed for reproducibility.
exclude_unknowns (bool, optional, default: True) – If True, unknown users and items will be ignored during model evaluation.
verbose (bool, optional, default: False) – Output running log.

evaluate(model, metrics, user_based, show_validation)[source]¶

Evaluate given models according to given metrics

Parameters:	model (`cornac.models.Recommender`) – Recommender model to be evaluated. metrics (`iterable`) – List of metrics. user_based (bool, required) – Evaluation strategy for the rating metrics. Whether results are averaging based on number of users or number of ratings. show_validation (bool, optional, default: True) – Whether to show the results on validation set (if exists).
Returns:	res
Return type:	`cornac.experiment.Result`

Propensity Stratified Evaluation¶

class cornac.eval_methods.propensity_stratified_evaluation.PropensityStratifiedEvaluation(data, test_size=0.2, val_size=0.0, n_strata=2, rating_threshold=1.0, seed=None, exclude_unknowns=True, verbose=False, **kwargs)[source]¶

Propensity-based Stratified Evaluation Method proposed by Jadidinejad et al. (2021)

Parameters:

data (array-like, required) – Raw preference data in the triplet format [(user_id, item_id, rating_value)].
test_size (float, optional, default: 0.2) – The proportion of the test set, if > 1 then it is treated as the size of the test set.
val_size (float, optional, default: 0.0) – The proportion of the validation set, if > 1 then it is treated as the size of the validation set.
n_strata (int, optional, default: 2) – The number of strata for propensity-based stratification.
rating_threshold (float, optional, default: 1.0) – Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected).
seed (int, optional, default: None) – Random seed for reproducibility.
exclude_unknowns (bool, optional, default: True) – If True, unknown users and items will be ignored during model evaluation.
verbose (bool, optional, default: False) – Output running log.

References

Amir H. Jadidinejad, Craig Macdonald and Iadh Ounis, The Simpson’s Paradox in the Offline Evaluation of Recommendation Systems, ACM Transactions on Information Systems (to appear) https://arxiv.org/abs/2104.08912

evaluate(model, metrics, user_based, show_validation=True)[source]¶

Evaluate given models according to given metrics

Parameters:	model (`cornac.models.Recommender`) – Recommender model to be evaluated. metrics (`iterable`) – List of metrics. user_based (bool, required) – Evaluation strategy for the rating metrics. Whether results are averaging based on number of users or number of ratings. show_validation (bool, optional, default: True) – Whether to show the results on validation set (if exists).
Returns:	res
Return type:	`cornac.experiment.Result`

cornac.eval_methods.propensity_stratified_evaluation.ranking_eval(model, metrics, train_set, test_set, val_set=None, rating_threshold=1.0, exclude_unknowns=True, verbose=False, props=None)[source]¶

Evaluate model on provided ranking metrics.

Parameters:

model (cornac.models.Recommender, required) – Recommender model to be evaluated.
metrics (iterable, required) – List of rating metrics cornac.metrics.RankingMetric.
train_set (cornac.data.Dataset, required) – Dataset to be used for model training. This will be used to exclude observations already appeared during training.
test_set (cornac.data.Dataset, required) – Dataset to be used for evaluation.
val_set (cornac.data.Dataset, optional, default: None) – Dataset to be used for model selection. This will be used to exclude observations already appeared during validation.
rating_threshold (float, optional, default: 1.0) – The threshold to convert ratings into positive or negative feedback.
exclude_unknowns (bool, optional, default: True) – Ignore unknown users and items during evaluation.
verbose (bool, optional, default: False) – Output evaluation progress.
props (dictionary, optional, default: None) – items propensity scores

Returns:

res –

Tuple of two lists:

average result for each of the metrics
average result per user for each of the metrics

Return type:

(List, List)

Ratio Split¶

class cornac.eval_methods.ratio_split.RatioSplit(data, test_size=0.2, val_size=0.0, rating_threshold=1.0, seed=None, exclude_unknowns=True, verbose=False, **kwargs)[source]¶

Splitting data into training, validation, and test sets based on provided sizes. Data is always shuffled before split.

Parameters:

data (array-like, required) – Raw preference data in the triplet format [(user_id, item_id, rating_value)].
test_size (float, optional, default: 0.2) – The proportion of the test set, if > 1 then it is treated as the size of the test set.
val_size (float, optional, default: 0.0) – The proportion of the validation set, if > 1 then it is treated as the size of the validation set.
rating_threshold (float, optional, default: 1.0) – Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected).
seed (int, optional, default: None) – Random seed for reproducibility.
exclude_unknowns (bool, optional, default: True) – If True, unknown users and items will be ignored during model evaluation.
verbose (bool, optional, default: False) – Output running log.

Stratified Split¶

class cornac.eval_methods.stratified_split.StratifiedSplit(data, group_by='user', chrono=False, fmt='UIRT', test_size=0.2, val_size=0.0, rating_threshold=1.0, seed=None, exclude_unknowns=True, verbose=False, **kwargs)[source]¶

Grouping data by user or item then splitting data into training, validation, and test sets.

Parameters:

data (array-like, required) – Raw preference data in the triplet format [(user_id, item_id, rating_value, timestamp)].
group_by (str, optional, default: 'user') – Grouping by ‘user’ or ‘item’.
chrono (bool, optional, default False) – Data is ordered by reviewed time or not. If this option is True, data must be in ‘UIRT’ format.
test_size (float, optional, default: 0.2) – The proportion of the test set, if > 1 then it is treated as the size of the test set.
val_size (float, optional, default: 0.0) – The proportion of the validation set, if > 1 then it is treated as the size of the validation set.
rating_threshold (float, optional, default: 1.0) – Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected).
seed (int, optional, default: None) – Random seed for reproducibility.
exclude_unknowns (bool, optional, default: True) – If True, unknown users and items will be ignored during model evaluation.
verbose (bool, optional, default: False) – Output running log.