alpenglow.offline package¶
Subpackages¶
- alpenglow.offline.evaluation package
- alpenglow.offline.models package
- Submodules
- alpenglow.offline.models.ALSFactorModel module
- alpenglow.offline.models.AsymmetricFactorModel module
- alpenglow.offline.models.FactorModel module
- alpenglow.offline.models.NearestNeighborModel module
- alpenglow.offline.models.PopularityModel module
- alpenglow.offline.models.SvdppModel module
- Module contents
Submodules¶
alpenglow.offline.OfflineModel module¶
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class
alpenglow.offline.OfflineModel.
OfflineModel
(**parameters)[source]¶ Bases:
alpenglow.ParameterDefaults.ParameterDefaults
OfflineModel is the base class for all traditional, scikit-learn style models in Alpenglow. Example usage:
data = pd.read_csv('data') train_data = data[data.time < (data.time.min()+250*86400)] test_data = data[ (data.time >= (data.time.min()+250*86400)) & (data.time < (data.time.min()+300*86400))] exp = ag.offline.models.FactorModel( learning_rate=0.07, negative_rate=70, number_of_iterations=9, ) exp.fit(data) test_users = list(set(test_data.user)&set(train_data.user)) recommendations = exp.recommend(users=test_users)
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fit
(X, y=None, columns={})[source]¶ Fit the model to a dataset.
Parameters: - X (pandas.DataFrame) – The input data, must contain the columns user and item. May contain the score column as well.
- y (pandas.Series or list) – The target values. If not set (and X doesn’t contain the score column), it is assumed to be constant 1 (implicit recommendation).
- columns (dict) – Optionally the mapping of the input DataFrame’s columns’ names to the expected ones.
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predict
(X)[source]¶ Predict the target values on X.
Parameters: X (pandas.DataFrame) – The input data, must contain the columns user and item. Returns: List of predictions Return type: list
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recommend
(users=None, k=100, exclude_known=True)[source]¶ Give toplist recommendations for users.
Parameters: - users (list) – List of users to give recommendation for.
- k (int) – Size of toplists
- exclude_known (bool) – Whether to exclude (user,item) pairs in the train dataset from the toplists.
Returns: DataFrame of recommendations, with columns user, item and rank.
Return type: pandas.DataFrame
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