FFMClassifier

Field-aware Factorization Machine for binary classification.

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

  Default → 10

  Dimensionality of the factorization or number of latent factors.

• weight_optimizer

  Type → optim.base.Optimizer | None

  Default → None

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

• latent_optimizer

  Type → optim.base.Optimizer | None

  Default → None

  The sequential optimizer used for updating the latent factors.

• loss

  Type → optim.losses.BinaryLoss | None

  Default → None

  The loss function to optimize for.

• sample_normalization

  Default → False

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

• l1_weight

  Default → 0.0

  Amount of L1 regularization used to push weights towards 0.

• l2_weight

  Default → 0.0

  Amount of L2 regularization used to push weights towards 0.

• l1_latent

  Default → 0.0

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

• l2_latent

  Default → 0.0

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

• intercept

  Default → 0.0

  Initial intercept value.

• intercept_lr

  Type → optim.base.Scheduler | float

  Default → 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

  Type → optim.initializers.Initializer | None

  Default → None

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

• latent_initializer

  Type → optim.initializers.Initializer | None

  Default → None

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

• clip_gradient

  Default → 1000000000000.0

  Clips the absolute value of each gradient value.

• seed

  Type → int | None

  Default → 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}, True),
    ({'user': 'Alice', 'item': 'Terminator', 'time': .13}, True),
    ({'user': 'Alice', 'item': 'Star Wars', 'time': .14}, True),
    ({'user': 'Alice', 'item': 'Notting Hill', 'time': .15}, False),
    ({'user': 'Alice', 'item': 'Harry Potter ', 'time': .16}, True),
    ({'user': 'Bob', 'item': 'Superman', 'time': .13}, True),
    ({'user': 'Bob', 'item': 'Terminator', 'time': .12}, True),
    ({'user': 'Bob', 'item': 'Star Wars', 'time': .16}, True),
    ({'user': 'Bob', 'item': 'Notting Hill', 'time': .10}, False)
)

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

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

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

True

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

Update the model with a set of features x and a label y.

Parameters

• x — 'dict'
• y — 'base.typing.ClfTarget'
• w — defaults to 1.0

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict'
• kwargs

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

• x

Returns

A dictionary that associates a probability which each label.

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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). ↩