RandomizedLogisticRegression

class ibex.sklearn.linear_model.RandomizedLogisticRegression(*args, **kwargs)

Bases: sklearn.linear_model.randomized_l1.RandomizedLogisticRegression, ibex._base.FrameMixin

Note

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

Randomized Logistic Regression

Randomized Logistic Regression works by subsampling the training data and fitting a L1-penalized LogisticRegression model where the penalty of a random subset of coefficients has been scaled. By performing this double randomization several times, the method assigns high scores to features that are repeatedly selected across randomizations. This is known as stability selection. In short, features selected more often are considered good features.

C : float or array-like of shape [n_reg_parameter], optional, default=1
The regularization parameter C in the LogisticRegression. When C is an array, fit will take each regularization parameter in C one by one for LogisticRegression and store results for each one in all_scores_, where columns and rows represent corresponding reg_parameters and features.
scaling : float, optional, default=0.5
The s parameter used to randomly scale the penalty of different features. Should be between 0 and 1.
sample_fraction : float, optional, default=0.75
The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used.
n_resampling : int, optional, default=200
Number of randomized models.
selection_threshold : float, optional, default=0.25
The score above which features should be selected.
tol : float, optional, default=1e-3
tolerance for stopping criteria of LogisticRegression
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 (e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default True
If True, the regressors X will be normalized before regression. This parameter is ignored when fit_intercept is set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if you wish to standardize, please use preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
random_state : int, RandomState instance or None, optional (default=None)
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.
n_jobs : integer, optional
Number of CPUs to use during the resampling. If ‘-1’, use all the CPUs
pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
memory : None, str or object with the joblib.Memory interface, optional (default=None)
Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory.
scores_ : array, shape = [n_features]
Feature scores between 0 and 1.
all_scores_ : array, shape = [n_features, n_reg_parameter]
Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests scores_ is the max of all_scores_.
>>> from sklearn.linear_model import RandomizedLogisticRegression
>>> randomized_logistic = RandomizedLogisticRegression()

Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417-473, September 2010 DOI: 10.1111/j.1467-9868.2010.00740.x

RandomizedLasso, LogisticRegression

fit(X, y)

Note

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

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. Will be cast to X’s dtype if necessary
self : object
Returns an instance of self.
fit_transform(X, y=None, **fit_params)

Note

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
inverse_transform(X)

Note

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

Reverse the transformation operation

X : array of shape [n_samples, n_selected_features]
The input samples.
X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.
transform(X)

Note

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

Reduce X to the selected features.

X : array of shape [n_samples, n_features]
The input samples.
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.