Struct PyLogisticRegression 

pub struct PyLogisticRegression { /* private fields */ }

Logistic Regression (aka logit, MaxEnt) classifier.

In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross-entropy loss if the ‘multi_class’ option is set to ‘multinomial’.

This class implements regularized logistic regression using various solvers. Note that regularization is applied by default.

Parameters

• penalty - One of “l1”, “l2”, “elasticnet”. Default: “l2”
• tol - Tolerance for stopping criteria. Default: 1e-4
• c - Inverse of regularization strength. Default: 1.0
• fit_intercept - Whether to add bias term. Default: true
• solver - One of “lbfgs”, “newton-cg”, “sag”, “saga”. Default: “lbfgs”
• max_iter - Maximum iterations. Default: 100
• multi_class - One of “auto”, “ovr”, “multinomial”. Default: “auto”
• random_state - Random seed for reproducibility
• l1_ratio - Elastic-Net mixing parameter (0 to 1)

References

• L-BFGS-B: http://users.iems.northwestern.edu/~nocedal/lbfgsb.html
• SAG: https://hal.inria.fr/hal-00860051/document
• SAGA: https://arxiv.org/abs/1407.0202

Trait Implementations

impl<'py> IntoPyObject<'py> for PyLogisticRegression

type Target = PyLogisticRegression

The Python output type

type Output = Bound<'py, <PyLogisticRegression as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl PyClass for PyLogisticRegression

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyLogisticRegression

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

const RAW_DOC: &'static CStr = /// Logistic Regression (aka logit, MaxEnt) classifier. /// /// In the multiclass case, the training algorithm uses the one-vs-rest (OvR) /// scheme if the 'multi_class' option is set to 'ovr', and uses the /// cross-entropy loss if the 'multi_class' option is set to 'multinomial'. /// /// This class implements regularized logistic regression using various solvers. /// **Note that regularization is applied by default**. /// /// # Parameters /// /// - `penalty` - One of "l1", "l2", "elasticnet". Default: "l2" /// - `tol` - Tolerance for stopping criteria. Default: 1e-4 /// - `c` - Inverse of regularization strength. Default: 1.0 /// - `fit_intercept` - Whether to add bias term. Default: true /// - `solver` - One of "lbfgs", "newton-cg", "sag", "saga". Default: "lbfgs" /// - `max_iter` - Maximum iterations. Default: 100 /// - `multi_class` - One of "auto", "ovr", "multinomial". Default: "auto" /// - `random_state` - Random seed for reproducibility /// - `l1_ratio` - Elastic-Net mixing parameter (0 to 1) /// /// # References /// /// - L-BFGS-B: <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html> /// - SAG: <https://hal.inria.fr/hal-00860051/document> /// - SAGA: <https://arxiv.org/abs/1407.0202>

Docstring for the class provided on the struct or enum.

const DOC: &'static CStr

Fully rendered class doc, including the text_signature if a constructor is defined.

type BaseType = PyAny

Base class

type ThreadChecker = SendablePyClass<PyLogisticRegression>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl PyClassNewTextSignature for PyLogisticRegression

const TEXT_SIGNATURE: &'static str = "(penalty=\"l2\", dual=False, tol=1e-4, c=1.0, fit_intercept=True, intercept_scaling=1.0, class_weight=None, random_state=None, solver=\"lbfgs\", max_iter=100, multi_class=\"auto\", verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)"

impl PyMethods<PyLogisticRegression> for PyClassImplCollector<PyLogisticRegression>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyLogisticRegression

const NAME: &'static str = "LogisticRegression"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.

impl DerefToPyAny for PyLogisticRegression

impl ExtractPyClassWithClone for PyLogisticRegression