class lasso_transformer:
def __init__(self,r,groups_ids):
self.lasso = GroupLasso( groups=groups_ids, group_reg=r, l1_reg=r, n_iter = 1000,
scale_reg="None", supress_warning=True, tol=1e-04,
)
def fit(self,X,y):
self.lasso.fit(X,y.values.reshape(-1, 1))
def transform(self,X,y=None):
return self.lasso.transform(X)
def fit_transform(self,X,y):
self.fit(X,y)
return self.transform(X,y)
)
print("Starting fit")
gl.LOG_LOSSES = True
gl.fit(X, y)
for i in range(w.shape[1]):
plt.figure()
plt.subplot(211)
plt.plot(w[:, i], ".", label="True weights")
plt.plot(gl.coef_[:, i], ".", label="Estimated weights")
plt.subplot(212)
plt.plot(w[gl.sparsity_mask, i], ".", label="True weights")
plt.plot(gl.coef_[gl.sparsity_mask, i], ".", label="Estimated weights")
plt.legend()
plt.figure()
plt.plot([w.min(), w.max()], [gl.coef_.min(), gl.coef_.max()], "gray")
plt.scatter(w, gl.coef_, s=10)
plt.ylabel("Learned coefficients")
plt.xlabel("True coefficients")
plt.figure()
plt.plot(gl.losses_)
print("X shape: {X.shape}".format(X=X))
print("Transformed X shape: {shape}".format(shape=gl.transform(X).shape))
print("True intercept: {intercept}".format(intercept=intercept))
print("Estimated intercept: {intercept}".format(intercept=gl.intercept_))
plt.show()