survival.svm.FastSurvivalSVM¶
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class
survival.svm.
FastSurvivalSVM
(alpha=1, rank_ratio=1.0, fit_intercept=False, max_iter=20, verbose=False, tol=None, optimizer=None, random_state=None, timeit=False)¶ Efficient Training of linear Survival Support Vector Machine
Training data consists of n triplets \((\mathbf{x}_i, y_i, \delta_i)\), where \(\mathbf{x}_i\) is a d-dimensional feature vector, \(y_i > 0\) the survival time or time of censoring, and \(\delta_i \in \{0,1\}\) the binary event indicator. Using the training data, the objective is to minimize the following function:
\[ \begin{align}\begin{aligned} \arg \min_{\mathbf{w}, b} \frac{1}{2} \mathbf{w}^\top \mathbf{w} + \frac{\alpha}{2} \left[ r \sum_{i,j \in \mathcal{P}} \max(0, 1 - (\mathbf{w}^\top \mathbf{x}_i - \mathbf{w}^\top \mathbf{x}_j))^2 + (1 - r) \sum_{i=0}^n \left( \zeta_{\mathbf{w}, b} (y_i, x_i, \delta_i) \right)^2 \right]\\\begin{split}\zeta_{\mathbf{w},b} (y_i, \mathbf{x}_i, \delta_i) = \begin{cases} \max(0, y_i - \mathbf{w}^\top \mathbf{x}_i - b) \quad \text{if $\delta_i = 0$,} \\ y_i - \mathbf{w}^\top \mathbf{x}_i - b \quad \text{if $\delta_i = 1$,} \\ \end{cases}\end{split}\\\mathcal{P} = \{ (i, j) \mid y_i > y_j \land \delta_j = 1 \}_{i,j=1,\dots,n}\end{aligned}\end{align} \]The hyper-parameter \(\alpha > 0\) determines the amount of regularization to apply: a smaller value increases the amount of regularization and a higher value reduces the amount of regularization. The hyper-parameter \(r \in [0; 1]\) determines the trade-off between the ranking objective and the regresson objective. If \(r = 1\) it reduces to the ranking objective, and if \(r = 0\) to the regression objective. If the regression objective is used, survival/censoring times are log-transform and thus cannot be zero or negative.
See
survival.svm.FastKernelSurvivalSVM
for an efficient implementation of kernel Survival Support Vector Machine.Parameters: alpha : float, positive (default: 1)
Weight of penalizing the squared hinge loss in the objective function
rank_ratio : float, optional (default=1.0)
Mixing parameter between regression and ranking objective with
0 <= rank_ratio <= 1
. Ifrank_ratio = 1
, only ranking is performed, ifrank_ratio = 0
, only regression is performed. A non-zero value is only allowed if optimizer is one of ‘avltree’, ‘rbtree’, or ‘direct-count’.fit_intercept : boolean, optional (default=False)
Whether to calculate an intercept for the regression model. If set to
False
, no intercept will be calculated. Has no effect ifrank_ratio = 1
, i.e., only ranking is performed.max_iter : int, optional (default: 20)
Maximum number of iterations to perform in Newton optimization
verbose : bool, optional (default: False)
Whether to print messages during optimization
tol : float, optional
Tolerance for termination. For detailed control, use solver-specific options.
optimizer : “avltree” | “direct-count” | “PRSVM” | “rbtree” | “simple”, optional (default: avltree)
Which optimizer to use.
random_state : int or
numpy.random.RandomState
instance, optionalRandom number generator (used to resolve ties in survival times).
timeit : False or int
If non-zero value is provided the time it takes for optimization is measured. The given number of repetitions are performed. Results can be accessed from the
optimizer_result_
attribute.References
[R16] Pölsterl, S., Navab, N., and Katouzian, A., “Fast Training of Support Vector Machines for Survival Analysis”, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, Lecture Notes in Computer Science, vol. 9285, pp. 243-259 (2015) Attributes
coef_: Coefficients of the features in the decision function. optimizer_result_: Stats returned by the optimizer. See scipy.optimize.optimize.OptimizeResult
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__init__
(alpha=1, rank_ratio=1.0, fit_intercept=False, max_iter=20, verbose=False, tol=None, optimizer=None, random_state=None, timeit=False)¶
Methods
__init__
([alpha, rank_ratio, fit_intercept, ...])fit
(X, y)Build a survival support vector machine model from training data. get_params
([deep])Get parameters for this estimator. predict
(X)Rank samples according to survival times score
(X, y)set_params
(**params)Set the parameters of this estimator. -
fit
(X, y)¶ Build a survival support vector machine model from training data.
Parameters: X : array-like, shape = [n_samples, n_features]
Data matrix.
y : structured array, shape = [n_samples]
A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.
Returns: self
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get_params
(deep=True)¶ Get parameters for this estimator.
Parameters: deep: boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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predict
(X)¶ Rank samples according to survival times
Lower ranks indicate shorter survival, higher ranks longer survival.
Parameters: X : array-like of shape = [n_samples, n_features]
The input samples.
Returns: y : array of shape = [n_samples]
Predicted ranks.
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set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self
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