Basic and Utility Algorithms¶
The lenskit.algorithms.basic module contains baseline and utility algorithms
for nonpersonalized recommendation and testing.
Personalized Mean Rating Prediction¶
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class
lenskit.algorithms.basic.Bias(items=True, users=True, damping=0.0)¶ Bases:
lenskit.algorithms.Predictor,lenskit.algorithms.TrainableA user-item bias rating prediction algorithm. This implements the following predictor algorithm:
\[s(u,i) = \mu + b_i + b_u\]where \(\mu\) is the global mean rating, \(b_i\) is item bias, and \(b_u\) is the user bias. With the provided damping values \(\beta_{\mathrm{u}}\) and \(\beta_{\mathrm{i}}\), they are computed as follows:
\[\begin{align*} \mu & = \frac{\sum_{r_{ui} \in R} r_{ui}}{|R|} & b_i & = \frac{\sum_{r_{ui} \in R_i} (r_{ui} - \mu)}{|R_i| + \beta_{\mathrm{i}}} & b_u & = \frac{\sum_{r_{ui} \in R_u} (r_{ui} - \mu - b_i)}{|R_u| + \beta_{\mathrm{u}}} \end{align*}\]The damping values can be interpreted as the number of default (mean) ratings to assume a priori for each user or item, damping low-information users and items towards a mean instead of permitting them to take on extreme values based on few ratings.
Parameters: - items – whether to compute item biases
- users – whether to compute user biases
- damping (number or tuple) – Bayesian damping to apply to computed biases. Either a number, to damp both user and item biases the same amount, or a (user,item) tuple providing separate damping values.
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predict(model, user, items, ratings=None)¶ Compute predictions for a user and items. Unknown users and items are assumed to have zero bias.
Parameters: - model (BiasModel) – the trained model to use.
- user – the user ID
- items (array-like) – the items to predict
- ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, will be used to recompute the user’s bias at prediction time.
Returns: scores for the items, indexed by item id.
Return type:
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class
lenskit.algorithms.basic.BiasModel¶ Trained model for the
Biasalgorithm.-
mean¶ the global mean.
Type: double
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items¶ the item means.
Type: pandas.Series
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users¶ the user means.
Type: pandas.Series
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Fallback Predictor¶
The Fallback rating predictor is a simple hybrid that takes a list of composite algorithms,
and uses the first one to return a result to predict the rating for each item.
A common case is to fill in with Bias when a primary predictor cannot score an item.
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class
lenskit.algorithms.basic.Fallback(*algorithms)¶ Bases:
lenskit.algorithms.Predictor,lenskit.algorithms.TrainableThe Fallback algorithm predicts with its first component, uses the second to fill in missing values, and so forth.
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load_model(file)¶ Save a trained model to a file.
Parameters: path (str) – the path to file from which to load the model. Returns: the re-loaded model (of an implementation-defined type).
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predict(model, user, items, ratings=None)¶ Compute predictions for a user and items.
Parameters: - model – the trained model to use. Either
Noneor the ratings matrix if the algorithm has no concept of training. - user – the user ID
- items (array-like) – the items to predict
- ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns: scores for the items, indexed by item id.
Return type: - model – the trained model to use. Either
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save_model(model, path)¶ Save a trained model to a file or directory. The default implementation pickles the model.
Algorithms are allowed to use any format for saving their models, including directories.
Parameters: - model – the trained model.
- path (str) – the path at which to save the model.
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train(ratings)¶ Train the model on rating/consumption data. Training methods that require additional data may accept it as additional parameters or via class members.
Parameters: ratings (pandas.DataFrame) – rating data, as a matrix with columns ‘user’, ‘item’, and ‘rating’. The user and item identifiers may be of any type. Returns: the trained model (of an implementation-defined type).
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