Struct PyMinMaxScaler 

pub struct PyMinMaxScaler { /* private fields */ }

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

The transformation is given by:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

where min, max = feature_range.

This transformation is often used as an alternative to zero mean, unit variance scaling.

Parameters

feature_range : tuple (min, max), default=(0, 1) Desired range of transformed data.

copy : bool, default=True Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).

clip : bool, default=False Set to True to clip transformed values of held-out data to provided feature range.

Attributes

min_ : ndarray of shape (n_features,) Per feature adjustment for minimum. Equivalent to min - X.min(axis=0) * self.scale_

scale_ : ndarray of shape (n_features,) Per feature relative scaling of the data. Equivalent to (max - min) / (X.max(axis=0) - X.min(axis=0))

data_min_ : ndarray of shape (n_features,) Per feature minimum seen in the data

data_max_ : ndarray of shape (n_features,) Per feature maximum seen in the data

data_range_ : ndarray of shape (n_features,) Per feature range (data_max_ - data_min_) seen in the data

n_features_in_ : int Number of features seen during :term:fit.

n_samples_seen_ : int The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.

Examples

from sklears_python import MinMaxScaler import numpy as np data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] scaler = MinMaxScaler() scaler.fit(data) MinMaxScaler() print(scaler.data_max_) [ 1. 18.] print(scaler.transform(data)) [[0. 0. ] [0.25 0.25] [0.5 0.5 ] [1. 1. ]] print(scaler.transform([[2, 2]])) [[1.5 0. ]]

Trait Implementations

impl DerefToPyAny for PyMinMaxScaler

impl ExtractPyClassWithClone for PyMinMaxScaler

impl<'py> IntoPyObject<'py> for PyMinMaxScaler

type Target = PyMinMaxScaler

The Python output type

type Output = Bound<'py, <PyMinMaxScaler 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 PyMinMaxScaler

const NAME: &str = "MinMaxScaler"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyMinMaxScaler

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

Module which the class will be associated with.

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 = /// Transform features by scaling each feature to a given range. /// /// This estimator scales and translates each feature individually such /// that it is in the given range on the training set, e.g. between /// zero and one. /// /// The transformation is given by: /// /// ```text /// X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) /// X_scaled = X_std * (max - min) + min /// ``` /// /// where min, max = feature_range. /// /// This transformation is often used as an alternative to zero mean, /// unit variance scaling. /// /// Parameters /// ---------- /// feature_range : tuple (min, max), default=(0, 1) /// Desired range of transformed data. /// /// copy : bool, default=True /// Set to False to perform inplace row normalization and avoid a /// copy (if the input is already a numpy array). /// /// clip : bool, default=False /// Set to True to clip transformed values of held-out data to /// provided `feature range`. /// /// Attributes /// ---------- /// min_ : ndarray of shape (n_features,) /// Per feature adjustment for minimum. Equivalent to /// ``min - X.min(axis=0) * self.scale_`` /// /// scale_ : ndarray of shape (n_features,) /// Per feature relative scaling of the data. Equivalent to /// ``(max - min) / (X.max(axis=0) - X.min(axis=0))`` /// /// data_min_ : ndarray of shape (n_features,) /// Per feature minimum seen in the data /// /// data_max_ : ndarray of shape (n_features,) /// Per feature maximum seen in the data /// /// data_range_ : ndarray of shape (n_features,) /// Per feature range ``(data_max_ - data_min_)`` seen in the data /// /// n_features_in_ : int /// Number of features seen during :term:`fit`. /// /// n_samples_seen_ : int /// The number of samples processed by the estimator. /// It will be reset on new calls to fit, but increments across /// ``partial_fit`` calls. /// /// Examples /// -------- /// >>> from sklears_python import MinMaxScaler /// >>> import numpy as np /// >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] /// >>> scaler = MinMaxScaler() /// >>> scaler.fit(data) /// MinMaxScaler() /// >>> print(scaler.data_max_) /// [ 1. 18.] /// >>> print(scaler.transform(data)) /// [[0. 0. ] /// [0.25 0.25] /// [0.5 0.5 ] /// [1. 1. ]] /// >>> print(scaler.transform([[2, 2]])) /// [[1.5 0. ]]

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 Layout = <<PyMinMaxScaler as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<PyMinMaxScaler>

Description of how this class is laid out in memory

type BaseType = PyAny

Base class

type ThreadChecker = NoopThreadChecker

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<PyObjectOffset>

Used to provide the dictoffset slot (equivalent to tp_dictoffset)

fn weaklist_offset() -> Option<PyObjectOffset>

Used to provide the weaklistoffset slot (equivalent to tp_weaklistoffset

impl PyClassNewTextSignature for PyMinMaxScaler

const TEXT_SIGNATURE: &'static str = "(feature_range=..., copy=True, clip=False)"

impl PyMethods<PyMinMaxScaler> for PyClassImplCollector<PyMinMaxScaler>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyMinMaxScaler

const NAME: &str = <Self as ::pyo3::PyClass>::NAME

👎Deprecated since 0.28.0:

prefer using ::type_object(py).name() to get the correct runtime value

Class name.

const MODULE: Option<&str> = <Self as ::pyo3::impl_::pyclass::PyClassImpl>::MODULE

👎Deprecated since 0.28.0:

prefer using ::type_object(py).module() to get the correct runtime value

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.