LarsCV
¶
-
class
ibex.sklearn.linear_model.
LarsCV
(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=1, eps=2.220446049250313e-16, copy_X=True, positive=False)¶ Bases:
sklearn.linear_model.least_angle.LarsCV
,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.LarsCV().fit(iris[features], iris['class']) >>> >>> prd.coef_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
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.LarsCV().fit(iris[features], iris['class']) >>> >>> #scalar intercept >>> type(prd.intercept_) <class 'numpy.float64'>
Cross-validated Least Angle Regression model
Read more in the User Guide.
- 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).
- verbose : boolean or integer, optional
- Sets the verbosity amount
- max_iter : integer, optional
- Maximum number of iterations to perform.
- normalize : boolean, optional, default True
- 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 | ‘auto’ | array-like
- Whether to use a precomputed Gram matrix to speed up
calculations. If set to
'auto'
let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X. - cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 3-fold 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,
KFold
is used.Refer User Guide for the various cross-validation strategies that can be used here.
- max_n_alphas : integer, optional
- The maximum number of points on the path used to compute the residuals in the cross-validation
- n_jobs : integer, optional
- Number of CPUs to use during the cross validation. If
-1
, use all the CPUs - eps : float, optional
- The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems.
- copy_X : boolean, optional, default True
- If
True
, X will be copied; else, it may be overwritten. - positive : boolean (default=False)
- Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default.
- coef_ : array, shape (n_features,)
- parameter vector (w in the formulation formula)
- intercept_ : float
- independent term in decision function
- coef_path_ : array, shape (n_features, n_alphas)
- the varying values of the coefficients along the path
- alpha_ : float
- the estimated regularization parameter alpha
- alphas_ : array, shape (n_alphas,)
- the different values of alpha along the path
- cv_alphas_ : array, shape (n_cv_alphas,)
- all the values of alpha along the path for the different folds
- mse_path_ : array, shape (n_folds, n_cv_alphas)
- the mean square error on left-out for each fold along the path
(alpha values given by
cv_alphas
) - n_iter_ : array-like or int
- the number of iterations run by Lars with the optimal alpha.
lars_path, LassoLars, LassoLarsCV
-
fit
(X, y)[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 the model using X, y as training data.
- X : array-like, shape (n_samples, n_features)
- Training data.
- y : array-like, shape (n_samples,)
- Target values.
- self : object
- returns an instance of 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