HOFMClassifier

Higher-Order Factorization Machine for binary classification.

The model equation is defined as:

\[\hat{y}(x) = w_{0} + \sum_{j=1}^{p} w_{j} x_{j} + \sum_{l=2}^{d} \sum_{j_1=1}^{p} \cdots \sum_{j_l=j_{l-1}+1}^{p} \left(\prod_{j'=1}^{l} x_{j_{j'}} \right) \left(\sum_{f=1}^{k_l} \prod_{j'=1}^{l} v_{j_{j'}, f}^{(l)} \right)\]

For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.

Parameters

• degree – defaults to 3

  Polynomial degree or model order.

• n_factors – defaults to 10

  Dimensionality of the factorization or number of latent factors.

• weight_optimizer (optim.Optimizer) – defaults to None

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

• latent_optimizer (optim.Optimizer) – defaults to None

  The sequential optimizer used for updating the latent factors.

• loss (optim.losses.BinaryLoss) – 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.schedulers.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.initializers.Initializer) – defaults to None

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

• latent_initializer (optim.initializers.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}, 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.HOFMClassifier(
...     degree=3,
...     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

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

learn_one

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

Parameters

• x (dict)
• y (Union[bool, str, int])
• sample_weight – defaults to 1.0

Returns

Classifier: self

predict_many

Predict the labels of a DataFrame X.

Parameters

• X (pandas.core.frame.DataFrame)

Returns

Series: Series of predicted labels.

predict_one

Predict the label of a set of features x.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int]: The predicted label.

predict_proba_many

Predict the labels of a DataFrame X.

Parameters

• X (pandas.core.frame.DataFrame)

Returns

DataFrame: DataFrame that associate probabilities which each label as columns.

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.

References

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1. Rendle, S., 2010, December. Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE. ↩