LinearSVR

class ibex.sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000)

Bases: sklearn.svm.classes.LinearSVR, 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 svm as pd_svm
>>>
>>> prd =  pd_svm.LinearSVR().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 svm as pd_svm
>>>
>>> prd = pd_svm.LinearSVR().fit(iris[features], iris['class'])
>>>
>>> #scalar intercept
>>> type(prd.intercept_)
<class 'numpy.float64'>

Linear Support Vector Regression.

Similar to SVR with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

This class supports both dense and sparse input.

Read more in the User Guide.

C : float, optional (default=1.0)
Penalty parameter C of the error term. The penalty is a squared l2 penalty. The bigger this parameter, the less regularization is used.
loss : string, ‘epsilon_insensitive’ or ‘squared_epsilon_insensitive’ (default=’epsilon_insensitive’)
Specifies the loss function. ‘l1’ is the epsilon-insensitive loss (standard SVR) while ‘l2’ is the squared epsilon-insensitive loss.
epsilon : float, optional (default=0.1)
Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0.
dual : bool, (default=True)
Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.
tol : float, optional (default=1e-4)
Tolerance for stopping criteria.
fit_intercept : boolean, optional (default=True)
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).
intercept_scaling : float, optional (default=1)
When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
verbose : int, (default=0)
Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling the data. 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.
max_iter : int, (default=1000)
The maximum number of iterations to be run.
coef_ : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is a readonly property derived from raw_coef_ that follows the internal memory layout of liblinear.

intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
>>> from sklearn.svm import LinearSVR
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = LinearSVR(random_state=0)
>>> regr.fit(X, y)
LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,
     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,
     random_state=0, tol=0.0001, verbose=0)
>>> print(regr.coef_)
[ 16.35750999  26.91499923  42.30652207  60.47843124]
>>> print(regr.intercept_)
[-4.29756543]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-4.29756543]
LinearSVC
Implementation of Support Vector Machine classifier using the same library as this class (liblinear).
SVR
Implementation of Support Vector Machine regression using libsvm: the kernel can be non-linear but its SMO algorithm does not scale to large number of samples as LinearSVC does.
sklearn.linear_model.SGDRegressor
SGDRegressor can optimize the same cost function as LinearSVR by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.
fit(X, y, sample_weight=None)[source]

Note

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

Fit the model according to the given training data.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
sample_weight : array-like, shape = [n_samples], optional
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
self : object
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.