alpenglow.offline package

Submodules

alpenglow.offline.OfflineModel module

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)
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.

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

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

Module contents