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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Predict using the linear model
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
- Samples.
- C : array, shape = (n_samples,)
- Returns predicted values.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter