FMClassifier¶
Factorization Machine for binary classification.
The model equation is defined as:
Where \(\mathbf{v}_j\) and \(\mathbf{v}_{j'}\) are \(j\) and \(j'\) latent vectors, respectively.
For more efficiency, this model automatically one-hot encodes strings features considering them as categorical variables.
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.BinaryLoss) – defaults to
None
The loss function to optimize for.
-
sample_normalization – defaults to
False
Whether to divide each element of
x
byx
'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 afloat
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'}, True),
... ({'user': 'Alice', 'item': 'Terminator'}, True),
... ({'user': 'Alice', 'item': 'Star Wars'}, True),
... ({'user': 'Alice', 'item': 'Notting Hill'}, False),
... ({'user': 'Alice', 'item': 'Harry Potter '}, True),
... ({'user': 'Bob', 'item': 'Superman'}, True),
... ({'user': 'Bob', 'item': 'Terminator'}, True),
... ({'user': 'Bob', 'item': 'Star Wars'}, True),
... ({'user': 'Bob', 'item': 'Notting Hill'}, False)
... )
>>> model = facto.FMClassifier(
... n_factors=10,
... seed=42,
... )
>>> for x, y in dataset:
... _ = model.learn_one(x, y)
>>> model.predict_one({'Bob': 1, 'Harry Potter': 1})
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.
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 (Union[bool, str, int])
- sample_weight – defaults to
1.0
Returns
Classifier: self
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_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.