FFMRegressor

Field-aware Factorization Machine for regression.

The model equation is defined by:

\[\hat{y}(x) = w_{0} + \sum_{j=1}^{p} w_{j} x_{j} + \sum_{j=1}^{p} \sum_{j'=j+1}^{p} \langle \mathbf{v}_{j, f_{j'}}, \mathbf{v}_{j', f_j} \rangle x_{j} x_{j'}\]

Where \(\mathbf{v}_{j, f_{j'}}\) is the latent vector corresponding to \(j\) feature for \(f_{j'}\) field, and \(\mathbf{v}_{j', f_j}\) is the latent vector corresponding to \(j'\) feature for \(f_j\) field.

For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables. Field names are inferred from feature names by taking everything before the first underscore: feature_name.split('_')[0].

Parameters

• n_factors – defaults to 10

  Dimensionality of the factorization or number of latent factors.

• weight_optimizer (optim.base.Optimizer) – defaults to None

  The sequential optimizer used for updating the feature weights. Note that the intercept is handled separately.

• latent_optimizer (optim.base.Optimizer) – defaults to None

  The sequential optimizer used for updating the latent factors.

• loss (optim.losses.RegressionLoss) – defaults to None

  The loss function to optimize for.

• sample_normalization – defaults to False

  Whether to divide each element of x by x's L2-norm.

• l1_weight – defaults to 0.0

  Amount of L1 regularization used to push weights towards 0.

• l2_weight – defaults to 0.0

  Amount of L2 regularization used to push weights towards 0.

• l1_latent – defaults to 0.0

  Amount of L1 regularization used to push latent weights towards 0.

• l2_latent – defaults to 0.0

  Amount of L2 regularization used to push latent weights towards 0.

• intercept – defaults to 0.0

  Initial intercept value.

• intercept_lr (Union[optim.base.Scheduler, float]) – defaults to 0.01

  Learning rate scheduler used for updating the intercept. An instance of optim.schedulers.Constant is used if a float is passed. No intercept will be used if this is set to 0.

• weight_initializer (optim.base.Initializer) – defaults to None

  Weights initialization scheme. Defaults to optim.initializers.Zeros().

• latent_initializer (optim.base.Initializer) – defaults to None

  Latent factors initialization scheme. Defaults to optim.initializers.Normal(mu=.0, sigma=.1, random_state=self.random_state).

• clip_gradient – defaults to 1000000000000.0

  Clips the absolute value of each gradient value.

• seed (int) – defaults to None

  Randomization seed used for reproducibility.

Attributes

• weights

  The current weights assigned to the features.

• latents

  The current latent weights assigned to the features.

Examples

>>> from river import facto

>>> dataset = (
...     ({'user': 'Alice', 'item': 'Superman', 'time': .12}, 8),
...     ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, 9),
...     ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, 8),
...     ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, 2),
...     ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, 5),
...     ({'user': 'Bob', 'item': 'Superman', 'time': .13}, 8),
...     ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, 9),
...     ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, 8),
...     ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, 2)
... )

>>> model = facto.FFMRegressor(
...     n_factors=10,
...     intercept=5,
...     seed=42,
... )

>>> for x, y in dataset:
...     model = model.learn_one(x, y)

>>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})
5.319945

>>> report = model.debug_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14})

>>> print(report)
Name                                       Value      Weight     Contribution
                               Intercept    1.00000    5.23501        5.23501
                                user_Bob    1.00000    0.11438        0.11438
                                    time    0.14000    0.03186        0.00446
    item_Harry Potter(time) - time(item)    0.14000    0.03153        0.00441
             user_Bob(time) - time(user)    0.14000    0.02864        0.00401
                       item_Harry Potter    1.00000    0.00000        0.00000
user_Bob(item) - item_Harry Potter(user)    1.00000   -0.04232       -0.04232

Methods

debug_one

Debugs the output of the FM regressor.

Parameters

• x (dict)
• decimals (int) – defaults to 5

Returns

str: A table which explains the output.

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

• x (dict)
• y (numbers.Number)
• sample_weight – defaults to 1.0

Returns

Regressor: self

predict_one

Predict the output of features x.

Parameters

• x

Returns

The prediction.

References

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1. Juan, Y., Zhuang, Y., Chin, W.S. and Lin, C.J., 2016, September. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 43-50). ↩