def test_SimuLinReg(self):
"""...Test simulation of a Linear Regression
"""
n_samples = 10
n_features = 3
idx = np.arange(n_features)
weights = np.exp(-idx / 10.)
weights[::2] *= -1
seed = 123
simu = SimuLinReg(weights, None, n_samples=n_samples, seed=seed,
verbose=False)
X, y = simu.simulate()
X_truth = np.array([[1.4912667, 0.80881799, 0.26977298], [
1.23227551, 0.50697013, 1.9409132
], [1.8891494, 1.49834791,
2.41445794], [0.19431319, 0.80245126, 1.02577552], [
-1.61687582, -1.08411865, -0.83438387
], [2.30419894, -0.68987056,
-0.39750262], [-0.28826405, -1.23635074, -0.76124386],
[-1.32869473, -1.8752391,
-0.182537], [0.79464218, 0.65055633, 1.57572506],
[0.71524202, 1.66759831, 0.88679047]])
y_truth = np.array([
-1.23590872, -5.1612244, -4.28171221, -1.00793646, 2.24652287,
-2.7766077, -0.20433269, 0.46957959, -2.37562537, 0.35124802
])
np.testing.assert_array_almost_equal(X_truth, X)
np.testing.assert_array_almost_equal(y_truth, y)
def create_model(model_type, n_samples, n_features, with_intercept=True):
weights = np.random.randn(n_features)
intercept = None
if with_intercept:
intercept = np.random.normal()
if model_type == 'Poisson':
# we need to rescale features to avoid overflows
weights /= n_features
if intercept is not None:
intercept /= n_features
if model_type == 'Linear':
simulator = SimuLinReg(weights,
intercept=intercept,
n_samples=n_samples,
verbose=False)
elif model_type == 'Logistic':
simulator = SimuLogReg(weights,
intercept=intercept,
n_samples=n_samples,
verbose=False)
elif model_type == 'Poisson':
simulator = SimuPoisReg(weights,
intercept=intercept,
n_samples=n_samples,
verbose=False)
labels, features = simulator.simulate()
if model_type == 'Linear':
model = ModelLinReg(fit_intercept=with_intercept)
elif model_type == 'Logistic':
model = ModelLogReg(fit_intercept=with_intercept)
elif model_type == 'Poisson':
model = ModelPoisReg(fit_intercept=with_intercept)
model.fit(labels, features)
return model
import matplotlib.pyplot as plt
import numpy as np
from tick.linear_model import SimuLinReg, SimuLogReg, SimuPoisReg
n_samples, n_features = 150, 2
weights0 = np.array([0.3, 1.2])
intercept0 = 0.5
simu_linreg = SimuLinReg(weights0,
intercept0,
n_samples=n_samples,
seed=123,
verbose=False)
X_linreg, y_linreg = simu_linreg.simulate()
simu_logreg = SimuLogReg(weights0,
intercept0,
n_samples=n_samples,
seed=123,
verbose=False)
X_logreg, y_logreg = simu_logreg.simulate()
simu_poisreg = SimuPoisReg(weights0,
intercept0,
n_samples=n_samples,
link='exponential',
seed=123,
verbose=False)
X_poisreg, y_poisreg = simu_poisreg.simulate()