RidgeCV¶

class ibex.sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False)

Bases: sklearn.linear_model.ridge.RidgeCV, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute coef_.

Example:

>>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.linear_model import LinearRegression as PdLinearRegression

>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])

>>> iris[features]
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
...

>>> from ibex.sklearn import linear_model as pd_linear_model
>>>
>>> prd =  pd_linear_model.RidgeCV().fit(iris[features], iris['class'])
>>>
>>> prd.coef_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64


Example:

>>> from ibex.sklearn import linear_model as pd_linear_model
>>> prd =  pd_linear_model.RidgeCV().fit(iris[features], iris[['class', 'class']])
>>>
>>> prd.coef_
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0...           0.414988          1.461297          -2.262141         -1.029095
1...           0.416640         -1.600833           0.577658         -1.385538
2...          -1.707525         -1.534268           2.470972          2.555382


Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute intercept_.

Example:

>>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.linear_model import LinearRegression as PdLinearRegression

>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])

>>> iris[features]
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
...

>>> from ibex.sklearn import linear_model as pd_linear_model
>>> prd = pd_linear_model.RidgeCV().fit(iris[features], iris[['class', 'class']])
>>>
>>> prd.intercept_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64


Ridge regression with built-in cross-validation.

By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation.

Read more in the User Guide.

alphas : numpy array of shape [n_alphas]
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to C^-1 in other linear models such as LogisticRegression or LinearSVC.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
normalize : boolean, optional, default False
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).
cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the efficient Leave-One-Out cross-validation
• integer, to specify the number of folds.
• An object to be used as a cross-validation generator.
• An iterable yielding train/test splits.

For integer/None inputs, if y is binary or multiclass, sklearn.model_selection.StratifiedKFold is used, else, sklearn.model_selection.KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

gcv_mode : {None, ‘auto’, ‘svd’, eigen’}, optional

Flag indicating which strategy to use when performing Generalized Cross-Validation. Options are:

'auto' : use svd if n_samples > n_features or when X is a sparse
matrix, otherwise use eigen
'svd' : force computation via singular value decomposition of X
(does not work for sparse matrices)
'eigen' : force computation via eigendecomposition of X^T X


The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending upon the shape and format of the training data.

store_cv_values : boolean, default=False
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Generalized Cross-Validation).
cv_values_ : array, shape = [n_samples, n_alphas] or shape = [n_samples, n_targets, n_alphas], optional
Cross-validation values for each alpha (if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
intercept_ : float | array, shape = (n_targets,)
Independent term in decision function. Set to 0.0 if fit_intercept = False.
alpha_ : float
Estimated regularization parameter.

Ridge: Ridge regression RidgeClassifier: Ridge classifier RidgeClassifierCV: Ridge classifier with built-in cross validation

fit(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit Ridge regression model

X : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values. Will be cast to X’s dtype if necessary
sample_weight : float or array-like of shape [n_samples]
Sample weight

self : Returns self.

predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict using the linear model

X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
C : array, shape = (n_samples,)
Returns predicted values.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : array-like, shape = (n_samples, n_features)
Test samples.
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.
score : float
R^2 of self.predict(X) wrt. y.