ElasticNet
¶
-
class
ibex.sklearn.linear_model.
ElasticNet
(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')¶ Bases:
sklearn.linear_model.coordinate_descent.ElasticNet
,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.ElasticNet().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.ElasticNet().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.ElasticNet().fit(iris[features], iris[['class', 'class']]) >>> >>> prd.intercept_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
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 * L1 + b * L2
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.
Read more in the User Guide.
- alpha : float, optional
- Constant that multiplies the penalty terms. Defaults to 1.0.
See the notes for the exact mathematical meaning of this
parameter.``alpha = 0`` is equivalent to an ordinary least square,
solved by the
LinearRegression
object. For numerical reasons, usingalpha = 0
with theLasso
object is not advised. Given this, you should use theLinearRegression
object. - l1_ratio : float
- The ElasticNet mixing parameter, with
0 <= l1_ratio <= 1
. Forl1_ratio = 0
the penalty is an L2 penalty.For l1_ratio = 1
it is an L1 penalty. For0 < l1_ratio < 1
, the penalty is a combination of L1 and L2. - fit_intercept : bool
- Whether the intercept should be estimated or not. If
False
, the data is assumed 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
. - precompute : True | False | array-like
- Whether to use a precomputed Gram matrix to speed up
calculations. The Gram matrix can also be passed as argument.
For sparse input this option is always
True
to preserve sparsity. - max_iter : int, optional
- The maximum number of iterations
- copy_X : boolean, optional, default True
- If
True
, X will be copied; else, it may be overwritten. - tol : float, optional
- The tolerance for the optimization: if the updates are
smaller than
tol
, the optimization code checks the dual gap for optimality and continues until it is smaller thantol
. - warm_start : bool, optional
- When set to
True
, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. - positive : bool, optional
- When set to
True
, forces the coefficients to be positive. - random_state : int, RandomState instance or None, optional, default None
- The seed of the pseudo random number generator that selects a random
feature to update. If int, random_state is the seed used by the random
number generator; If RandomState instance, random_state is the random
number generator; If None, the random number generator is the
RandomState instance used by np.random. Used when
selection
== ‘random’. - selection : str, default ‘cyclic’
- If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.
- coef_ : array, shape (n_features,) | (n_targets, n_features)
- parameter vector (w in the cost function formula)
- sparse_coef_ : scipy.sparse matrix, shape (n_features, 1) | (n_targets, n_features)
sparse_coef_
is a readonly property derived fromcoef_
- intercept_ : float | array, shape (n_targets,)
- independent term in decision function.
- n_iter_ : array-like, shape (n_targets,)
- number of iterations run by the coordinate descent solver to reach the specified tolerance.
>>> from sklearn.linear_model import ElasticNet >>> from sklearn.datasets import make_regression >>> >>> X, y = make_regression(n_features=2, random_state=0) >>> regr = ElasticNet(random_state=0) >>> regr.fit(X, y) ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=0, selection='cyclic', tol=0.0001, warm_start=False) >>> print(regr.coef_) [ 18.83816048 64.55968825] >>> print(regr.intercept_) 1.45126075617 >>> print(regr.predict([[0, 0]])) [ 1.45126076]
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array.
SGDRegressor: implements elastic net regression with incremental training. SGDClassifier: implements logistic regression with elastic net penalty
(SGDClassifier(loss="log", penalty="elasticnet")
).-
fit
(X, y, check_input=True)[source]¶ 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 model with coordinate descent.
- X : ndarray or scipy.sparse matrix, (n_samples, n_features)
- Data
- y : ndarray, shape (n_samples,) or (n_samples, n_targets)
- Target. Will be cast to X’s dtype if necessary
- check_input : boolean, (default=True)
- Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
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.
- 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