`sklearn_pmml_model.linear_model`#

The sklearn_pmml_model.linear_model module implements generalized linear models.

Submodules#

Package Contents#

Classes#

`PMMLLinearRegression`	Ordinary least squares Linear Regression.
`PMMLLogisticRegression`	Logistic Regression (aka logit, MaxEnt) classifier.
`PMMLRidge`	Linear least squares with l2 regularization.
`PMMLRidgeClassifier`	Classifier using Ridge regression.
`PMMLLasso`	Linear Model trained with L1 prior as regularizer (aka the Lasso).
`PMMLElasticNet`	Linear regression with combined L1 and L2 priors as regularizer.

class sklearn_pmml_model.linear_model.PMMLLinearRegression(pmml)#

Bases: sklearn_pmml_model.base.OneHotEncodingMixin, sklearn_pmml_model.base.PMMLBaseRegressor, sklearn.linear_model.LinearRegression

Ordinary least squares Linear Regression.

The PMML model consists out of a <RegressionModel> element, containing at least one <RegressionTable> element. Every table element contains a <NumericPredictor> element for numerical fields and <CategoricalPredictor> per value of a categorical field, describing the coefficients.

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/Regression.html

fit(x, y)#: Not supported: PMML models are already fitted.

_more_tags()#

class sklearn_pmml_model.linear_model.PMMLLogisticRegression(pmml)#

Bases: sklearn_pmml_model.base.OneHotEncodingMixin, sklearn_pmml_model.base.PMMLBaseClassifier, sklearn.linear_model.LogisticRegression

Logistic Regression (aka logit, MaxEnt) classifier.

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/Regression.html

fit(x, y)#: Not supported: PMML models are already fitted.

_more_tags()#

class sklearn_pmml_model.linear_model.PMMLRidge(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, solver='auto', positive=False, random_state=None)#

Bases: sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor, sklearn.linear_model.Ridge

Linear least squares with l2 regularization.

Minimizes the objective function:

||y - Xw||^2_2 + alpha * ||w||^2_2

This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape (n_samples, n_targets)).

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html

fit(x, y)#

Fit Ridge regression model.

Parameters:

X{ndarray, sparse matrix} of shape (n_samples, n_features): Training data.
yndarray of shape (n_samples,) or (n_samples, n_targets): Target values.
sample_weightfloat or ndarray of shape (n_samples,), default=None: Individual weights for each sample. If given a float, every sample will have the same weight.

Returns:

selfobject: Fitted estimator.

_more_tags()#

class sklearn_pmml_model.linear_model.PMMLRidgeClassifier(pmml)#

Bases: sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearClassifier, sklearn.linear_model.RidgeClassifier

Classifier using Ridge regression.

This classifier first converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html

fit(x, y)#

Fit Ridge classifier model.

Parameters:

X{ndarray, sparse matrix} of shape (n_samples, n_features): Training data.
yndarray of shape (n_samples,): Target values.
sample_weightfloat or ndarray of shape (n_samples,), default=None: Individual weights for each sample. If given a float, every sample will have the same weight.

New in version 0.17: sample_weight support to RidgeClassifier.

Returns:

selfobject: Instance of the estimator.

_more_tags()#

class sklearn_pmml_model.linear_model.PMMLLasso(pmml)#

Bases: sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor, sklearn.linear_model.Lasso

Linear Model trained with L1 prior as regularizer (aka the Lasso).

The optimization objective for Lasso is:

(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

Technically the Lasso model is optimizing the same objective function as the Elastic Net with l1_ratio=1.0 (no L2 penalty).

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html

fit(x, y)#

Fit model with coordinate descent.

Parameters:

X{ndarray, sparse matrix} of (n_samples, n_features): Data.
yndarray of shape (n_samples,) or (n_samples, n_targets): Target. Will be cast to X’s dtype if necessary.
sample_weightfloat or array-like of shape (n_samples,), default=None: Sample weights. Internally, the sample_weight vector will be rescaled to sum to n_samples.

New in version 0.23.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

selfobject: Fitted estimator.

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

_more_tags()#

class sklearn_pmml_model.linear_model.PMMLElasticNet(pmml)#

Bases: sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor, sklearn.linear_model.ElasticNet

Linear regression with combined L1 and L2 priors as regularizer.

Minimizes the objective function:

1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2

If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to:

a * ||w||_1 + 0.5 * b * ||w||_2^2

where:

alpha = a + b and l1_ratio = a / (a + b)

The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = 1 is the lasso penalty. Currently, l1_ratio <= 0.01 is not reliable, unless you supply your own sequence of alpha.

Parameters:

pmmlstr, object: Filename or file object containing PMML data.

Notes

Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html

fit(x, y)#

Fit model with coordinate descent.

Parameters:

X{ndarray, sparse matrix} of (n_samples, n_features): Data.
yndarray of shape (n_samples,) or (n_samples, n_targets): Target. Will be cast to X’s dtype if necessary.
sample_weightfloat or array-like of shape (n_samples,), default=None: Sample weights. Internally, the sample_weight vector will be rescaled to sum to n_samples.

New in version 0.23.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

selfobject: Fitted estimator.

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

_more_tags()#

sklearn_pmml_model.linear_model#

Submodules#

Package Contents#

Classes#

`sklearn_pmml_model.linear_model`#