class STLSQ¶
- class deeptime.sindy.STLSQ(threshold=0.1, alpha=0.05, max_iter=20, ridge_kw=None, normalize=False, fit_intercept=False, copy_X=True)¶
Sequentially thresholded least squares algorithm.
Attempts to minimize the objective function \(\|y - Xw\|^2_2 + \alpha \|w\|^2_2\) by iteratively performing least squares and masking out elements of the weight that are below a given threshold.
See this paper for more details [1].
- Parameters:
threshold (float, optional, default=0.1) – Minimum magnitude for a coefficient in the weight vector. Coefficients with magnitude below the threshold are set to zero.
alpha (float, optional, default=0.05) – Optional L2 (ridge) regularization on the weight vector.
max_iter (int, optional, default=20) – Maximum iterations of the optimization algorithm.
ridge_kw (dict, optional, default=None) – Optional keyword arguments to pass to the ridge regression.
fit_intercept (boolean, optional, default=False) – Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.
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.copy_X (boolean, optional, default=True) – If True, X will be copied; else, it may be overwritten.
References
Methods
fit
(x_, y)Fit to the data.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict using the linear model.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
- fit(x_, y)¶
Fit to the data.
- Parameters:
x (array-like, shape (n_samples, n_features)) – Training data (\(X\) in the above equation).
y (array-like, shape (n_samples,) or (n_samples, n_targets)) – Target values (\(y\) in the above equation).
- Returns:
self – Reference to self
- Return type:
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters:
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
params – Parameter names mapped to their values.
- Return type:
dict
- predict(X)¶
Predict using the linear model.
- Parameters:
X (array-like or sparse matrix, shape (n_samples, n_features)) – Samples.
- Returns:
C – Returns predicted values.
- Return type:
array, shape (n_samples,)
- score(X, y, sample_weight=None)¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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.- Parameters:
X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns:
score – \(R^2\) of
self.predict(X)
wrt. y.- Return type:
float
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance