collasso.simulate module#

Simulation.

Main function:: simulate - simulating feature and target matrices for multi-task learning

Examples#

>>> from collasso import CoopLassoCV
>>> x_train, y_train, x_test, y_test, beta = simulate()
>>> model = CoopLassoCV()
>>> model.fit(x_train, y_train)
>>> model.predict(x_test)

collasso.simulate.simulate(*, n0: int = 100, n1: int = 10000, p: int = 200, q: int = 3, rho: float = 0.9, kappa: float = 1.0, prob_com: float = 0.05, prob_sep: float = 0.05) → tuple[ndarray, ndarray, ndarray, ndarray, ndarray]#

Simulate Data for Linear Multi-Task Regression.

Simulates feature matrix and target matrix, with given probabilities of (i) common effects on all targets and (ii) specific effects on one target.

Parameters:

n0int, default=100: Number of training samples.
n1int, default=10000: Number of testing samples.
pint, default=200: Number of features.
qint, default=3: Number of targets.
rhofloat, default=0.90: Correlation coefficient, 0<=rho<=1.
kappafloat, default=1.00: Correlation coefficient, 0<=kappa<=1.
prob_comfloat, default=0.05: Probability of common effects for all targets, 0<=prob_com<=1.
prob_sepfloat, default=0.05: Probability of separate effects for each target.

Returns:

x_trainndarray of shape (n0_samples,p_features) or (n0_samples,p_features,q_targets): Training feature matrix or matrices, common matrix for all targets (if kappa=1) or separate matrix for each target (if 0<=kappa<1).
y_trainndarray of shape (n0_samples,q_targets): Training target matrix.
x_testndarray of shape (n1_samples,p_features) or (n1_samples,p_features,q_targets): Test feature matrix or matrices, common matrix for all targets (if kappa=1) or separate matrix for each target (if 0<=kappa<1).
y_testndarray of shape (n1_samples,q_targets): Test target matrix.
betandarray of shape (p_features,q_targets): True effects in the training and the test data (of the feature in the row on the target in the column).

Raises:

ValueError