myfm.MyFMOrderedProbit

class myfm.MyFMOrderedProbit(rank: int, init_stdev: float = 0.1, random_seed: int = 42, alpha_0: float = 1, beta_0: float = 1, gamma_0: float = 1, mu_0: float = 0, reg_0: float = 1, fit_w0: bool = True, fit_linear: bool = True)

Bases: myfm.gibbs.MyFMGibbsBase

Bayesian Factorization Machines for Ordinal Regression Tasks.

__init__(rank: int, init_stdev: float = 0.1, random_seed: int = 42, alpha_0: float = 1, beta_0: float = 1, gamma_0: float = 1, mu_0: float = 0, reg_0: float = 1, fit_w0: bool = True, fit_linear: bool = True)

Setup the configuration.

Parameters

• rank (int) – The number of factors.

• init_stdev (float, optional (defalult = 0.1)) – The standard deviation for initialization. The factorization machine weights are randomely sampled from Normal(0, init_stdev ** 2).

• random_seed (integer, optional (default = 0.1)) – The random seed used inside the whole learning process.

• alpha_0 (float, optional (default = 1.0)) – The half of alpha parameter for the gamma-distribution prior for alpha, lambda_w and lambda_V. Together with beta_0, the priors for these parameters are alpha, lambda_w, lambda_v ~ Gamma(alpha_0 / 2, beta_0 / 2)

• beta_0 (float, optional (default = 1.0)) – See the explanation for alpha_0 .

• gamma_0 (float, optional (default = 1.0)) – Inverse variance of the prior for mu_w, mu_v. Together with mu_0, the priors for these parameters are mu_w, mu_v ~ Normal(mu_0, 1 / gamma_0)

• mu_0 (float, optional (default = 0.0)) – See the explanation for gamma_0.

• reg_0 (float, optional (default = 0.0)) – Inverse variance of the prior for w0. w0 ~ Normal(0, 1 / reg_0)

• fit_w0 (bool, optional (default = True)) – whether to fit w0, by default True.

• fit_linear (bool, optional (default = True)) – whether to fit linear coefficients, by default True.

Methods

__init__(rank[, init_stdev, random_seed, ...])	Setup the configuration.
fit(X, y[, X_rel, X_test, y_test, ...])
get_hyper_trace()
predict(X[, X_rel])	Predict the class outcome according to the class probability.
predict_proba(X[, X_rel, n_workers])	Compute the ordinal class probability.

Attributes

V_samples	Obtain the Gibbs samples for factorized quadratic coefficient V.
cutpoint_samples	Obtain samples for the cutpoints.
w0_samples	Obtain samples for global bias w0.
w_samples	Obtain the Gibbs samples for linear coefficients w.

property V_samples: Optional[object]

Obtain the Gibbs samples for factorized quadratic coefficient V. Returns None if the model is not fit yet.

Returns:

Samples for lienar coefficients. The first dimension is for the sample index, the second for the feature index, and the third for the factorized dimension.

property cutpoint_samples: Optional[object]

Obtain samples for the cutpoints. If the model is not fit yet, return None.

Returns:

Samples for cutpoints.

predict(X: Union[numpy.ndarray, scipy.sparse.csr.csr_matrix], X_rel: List[myfm._myfm.RelationBlock] = []) → object

Predict the class outcome according to the class probability.

Parameters

• X (array_like) – The input data.

• X_rel (List[RelationBlock], optional) – Relational Block part of the data., by default []

Returns

The class prediction

Return type

np.int64

predict_proba(X: Union[numpy.ndarray, scipy.sparse.csr.csr_matrix], X_rel: List[myfm._myfm.RelationBlock] = [], n_workers: Optional[int] = None) → numpy.ndarray

Compute the ordinal class probability.

Parameters

• X (array_like) – The input data.

• X_rel (List[RelationBlock], optional) – Relational Block part of the data., by default []

Returns

The class probability

Return type

np.float

property w0_samples: Optional[object]

Obtain samples for global bias w0. If the model is not fit yet, return None.

Returns:

Samples for lienar coefficients.

property w_samples: Optional[object]

Obtain the Gibbs samples for linear coefficients w. Returns None if the model is not fit yet.

Returns:

Samples for lienar coefficients. The first dimension is for the sample index, and the second for the feature index.