pyrfm.linear_model.SparseMBRegressor

class pyrfm.linear_model.SparseMBRegressor(n_components=1000, loss='squared', solver='cd', C=1.0, alpha=1.0, fit_intercept=True, max_iter=100, tol=1e-06, eps=0.0001, warm_start=False, random_state=None, verbose=True, shuffle=True)

Bases: pyrfm.linear_model.sparse_mb_predictor.BaseSparseMBEstimator, pyrfm.linear_model.base.LinearRegressorMixin

Linear regression with feature map approximating the intersection (min) kernel by sparse explicit feature map, which was proposed by S.Maji and A.C.Berg. For more detail, see [1].

Parameters

• n_components (int (default=1000)) – Number of Monte Carlo samples per original features. Equals the dimensionality of the computed (mapped) feature space.

• loss (str (default="squared")) –

  Which loss function to use. Following losses can be used:

  • ’squared’

• C (double (default=1.0)) – Weight of loss term.

• alpha (double (default=1.0)) – Weight of the penalty term.

• fit_intercept (bool (default=True)) – Whether to fit intercept (bias term) or not.

• max_iter (int (default=100)) – Maximum number of iterations.

• tol (double (default=1e-6)) – Tolerance of stopping criterion. If sum of absolute val of update in one epoch is lower than tol, the AdaGrad solver stops learning.

• eps (double (default=1e-2)) – A small double to ensure objective function convex.

• warm_start (bool (default=False)) – Whether to activate warm-start or not.

• random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• verbose (bool (default=True)) – Verbose mode or not.

• shuffle (boole (default=True)) – Whether to shuffle the order of parameters for optimization or not.

self.transformer

The learned transformer for random feature maps.

Type

scikit-learn TransformMixin object.

self.coef_

The learned coefficients of the linear model.

Type

array, shape (n_components, )

self.intercept_

The learned intercept (bias) of the linear model.

Type

array, shape (1, )

References

[1] Max-Margin Additive Classifiers for Detection Subhransu Maji, Alexander C. Berg. In ICCV 2009. (http://acberg.com/papers/mb09iccv.pdf)

LOSSES = {'squared': <pyrfm.linear_model.loss_fast.Squared object>}

fit(X, y)

Fit model according to X and y.

Parameters

• X (array-like, shape = [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like, shape = [n_samples]) – Target values.

Returns

self – Returns self.

Return type

classifier

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

predict(X)

Perform regression on an array of test vectors X.

Parameters

X (array-like, shape = [n_samples, n_features]) –

Returns

Predicted target values for X

Return type

array, shape = [n_samples]

score(X, y, sample_weight=None)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters

• X (array-like, shape = (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like, shape = (n_samples) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like, shape = [n_samples], optional) – Sample weights.

Returns

score – R^2 of self.predict(X) wrt. y.

Return type

float

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with metrics.r2_score. This will influence the score method of all the multioutput regressors (except for multioutput.MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call metrics.r2_score directly or make a custom scorer with metrics.make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

Return type

self