def test_poisson(self):
model = PoissonRegressor()
y = Poisson().fitted(self.eta)
model.fit(self.X, y)
y_hat = model.predict(self.X)
diff = y_hat - y
rsq = 1 - np.mean(diff**2) / np.mean((y-np.mean(y))**2)
assert_true(rsq > .9)
def test_poisson_exposure(self):
model = PoissonRegressor()
exposure = np.random.exponential(scale=10, size=100)
y = Poisson().fitted(self.eta + np.log(exposure))
model.fit(self.X, y, exposure=exposure)
y_hat = model.predict(self.X, exposure=exposure)
diff = y_hat - y
rsq = 1 - np.mean(diff**2) / np.mean((y-np.mean(y))**2)
assert_true(rsq > .9)
def test_with_pipeline(self):
model = Pipeline([('PCA',PCA()), ('Poisson',PoissonRegressor())])
y = Poisson().fitted(self.eta)
model.fit(self.X, y)
y_hat = model.predict(self.X)
diff = y_hat - y
rsq = 1 - np.mean(diff**2) / np.mean((y-np.mean(y))**2)
assert_true(rsq > .9)
assert_equal(str(model), '''Pipeline(PCA=PCA(copy=True, n_components=None, whiten=False), PCA__copy=True,
PCA__n_components=None, PCA__whiten=False, Poisson=PoissonRegressor())''')
